Habib N. Najm

Senior Scientist, Combustion Research Facility

Author profile picture

Senior Scientist, Combustion Research Facility

hnnajm@sandia.gov

Biography

Habib Najm is a Senior Scientist at Sandia National Laboratory. He leads a research program in computational chemical sciences and uncertainty quantification, funded by the U.S. Department of Energy. He joined Sandia National Laboratories in 1993, and has worked in numerous computational science research areas, broadly focusing on the development of numerical methods and computational tools, and their application in scientific studies.

Habib has ongoing work in stochastic chemical modeling; uncertainty quantification and Bayesian inference with application in a range of chemical and materials science disciplines; optimal experimental design with application in chemical experiments; dynamical analysis of gas and surface chemical models; and machine learning with application in quantum chemistry for hydrocarbons. He is author/co-author of over 100 journal articles and holds eleven US patents.

Education

PhDMechanical EngineeringMassachusetts Institute of Technology1989
SMMechanical Engineering Massachusetts Institute of Technology1986
BEMechanical EngineeringAmerican University of Beirut1983

Research Interests

  • Computational chemistry & materials science
  • Numerical methods & computational software for uncertainty quantification
  • Statistical learning & data analysis in the physical sciences
  • Machine learning & data science in computational studies in chemistry & materials

Professional Organizations & Leadership

American Physical SocietyMember
Society of Industrial and Applied MathematicsMember
Combustion InstituteMember
American University of Beirut, LebanonMember, Department Advisory Board, Mechanical Engineering Department
Auburn UniversityAffiliated Professor, Dept. of Mathematics and Statistics
East Asian Journal of Applied MathematicsAssociate Editor
International Journal for Uncertainty Quantification, Begellhouse, Inc.Editor in Chief
Journal of Uncertainty Quantification, SIAM-ASAAssociate Editor
American Institute of Mathematical SciencesAssociate Editor, Foundations of Data Science

Awards

  • Fellow, Combustion Institute
  • O.W. Adams Award for Outstanding Achievement in Combustion Science, Sandia National Laboratories
  • Distinguished Alumnus Award, American University of Beirut, Lebanon

Publications

  • Williams, L., Sargsyan, K., Rohskopf, A., Najm, H.N., & Najm, H.N. (2024). Active learning for SNAP interatomic potentials via Bayesian predictive uncertainty. Computational Materials Science, 242. https://doi.org/10.1016/j.commatsci.2024.113074 Publication ID: 124820
  • Diaz-Ibarra, O.H., Kim, K., Najm, H.N., Safta, C., & Safta, C. (2024). CSPlib: A performance portable parallel software toolkit for analyzing complex kinetic mechanisms. Computer Physics Communications, 297. https://doi.org/10.1016/j.cpc.2023.109069 Publication ID: 123028
  • Dingreville, R.P.M., Desai, S., Shrivastava, A., Najm, H.N., D’Elia, M., & D’Elia, M. (2023). Trade-offs in the latent representation of microstructure evolution. Acta Materialia, 263(1). https://doi.org/10.1016/j.actamat.2023.119514 Publication ID: 122452
  • Bahr-Mueller, J.N., Sargsyan, K., Najm, H.N., & Najm, H.N. (2023). Polynomial Chaos Surrogate Construction for Stochastic Models with Parametric Uncertainty [Conference Paper]. https://www.osti.gov/biblio/2432080 Publication ID: 131168
  • Bahr-Mueller, J.N., Sargsyan, K., Najm, H.N., & Najm, H.N. (2023). Joint PCE Surrogate Construction with Uncertainty Quantification for Parameterized Stochastic Processes [Conference Presenation]. https://doi.org/10.2172/2430772 Publication ID: 126640
  • Diaz-Ibarra, O.H., Sargsyan, K., Najm, H.N., & Najm, H.N. (2023). Dimensionality Reduction and Weight-Parameterized Neural NetworkSurrogates for Climate Models [Conference Presenation]. https://doi.org/10.2172/2430572 Publication ID: 125944
  • Bahr-Mueller, J.N., Najm, H.N., Sargsyan, K., & Sargsyan, K. (2023). A joint PCE Surrogate Construction with Uncertainty Quantification for Parameterized Stochastic Processes Applied to Catalysis [Conference Presenation]. https://doi.org/10.2172/2431031 Publication ID: 127568
  • Nemer, M., Domski, P., Najm, H.N., & Najm, H.N. (2023). Bayesian Inference Applied to Phase-3 and Phase-5 Solubility [Conference Presenation]. https://doi.org/10.2172/2430983 Publication ID: 127396
  • Shrivastava, A., Kalaswad, M., Custer, J.O., Adams, D.P., Najm, H.N., & Najm, H.N. (2023). Bayesian optimization-assisted sputter deposition of Molybdenum thin films [Conference Presenation]. https://doi.org/10.2172/2431116 Publication ID: 127872
  • Zador, J., Najm, H.N., Yang, Y., & Yang, Y. (2023). Multifidelity Neural Network Formulations for Prediction of Reactive Molecular Potential Energy Surfaces. Journal of Chemical Information and Modeling, 63(8), pp. 2281-2295. https://doi.org/10.1021/acs.jcim.2c01617 Publication ID: 124080
  • Sargsyan, K., Najm, H.N., Bahr-Mueller, J.N., & Bahr-Mueller, J.N. (2023). A Polynomial Chaos Surrogate Construction for Chemical Catalysis Models [Conference Poster]. https://doi.org/10.2172/2431385 Publication ID: 128844
  • Desai, S., Shrivastava, A., D’Elia, M., Najm, H.N., Dingreville, R.P.M., & Dingreville, R.P.M. (2023). Latent dimension representations of microstructure evolution [Conference Presenation]. https://doi.org/10.2172/2431665 Publication ID: 129720
  • Aliod, C.M., Michelsen, H.A., Najm, H.N., Zador, J., & Zador, J. (2023). Comprehensive Kinetics on the C7H7 Potential Energy Surface under Combustion Conditions. Journal of Physical Chemistry. A, Molecules, Spectroscopy, Kinetics, Environment, and General Theory, 127(8), pp. 1941-1959. https://doi.org/10.1021/acs.jpca.2c08035 Publication ID: 123652
  • Robbe, P., Andersson, D., Bonnet, L.A.P.L., Casey, T., Cooper, M.W.D., Matthews, C., Sargsyan, K., Najm, H.N., & Najm, H.N. (2023). Surrogate-assisted Data-Free Inference with summary statistics for predicting xenon diffusivity in uranium oxide nuclear fuel [Conference Presenation]. https://doi.org/10.2172/2431859 Publication ID: 130436
  • Zador, J., Aliod, C.M., van de Vijver, R., Johansen, S.L., Yang, Y., Michelsen, H.A., Najm, H.N., & Najm, H.N. (2023). Automated Reaction Kinetics of Gas-Phase Organic Species over Multiwell Potential Energy Surfaces. Journal of Physical Chemistry A, 127(3), pp. 565-588. https://doi.org/10.1021/acs.jpca.2c06558 Publication ID: 124060
  • Najm, H.N., Sargsyan, K., D’Elia, M., & D’Elia, M. (2023). The role of stiffness in training and generalization of ResNets. Journal of Machine Learning for Modeling and Computing, 4(2), pp. 75-103. https://doi.org/10.1615/jmachlearnmodelcomput.2023047131 Publication ID: 123168
  • Han, X., Najm, H.N., & Najm, H.N. (2022). Modeling Fast Diffusion Processes in Time Integration of Stiff Stochastic Differential Equations. Communications on Applied Mathematics and Computation, 4(4), pp. 1457-1493. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85132437736&origin=inward Publication ID: 71901
  • Kalaswad, M., Shrivastava, A., Desai, S., Custer, J.O., Khan, R.M., Addamane, S.J., Monti, J.M., Fowler, J.E., Rodriguez, M.A., Delrio, F.W., Kotula, P.G., D’Elia, M., Najm, H.N., Dingreville, R.P.M., Boyce, B.L., Adams, D.P., & Adams, D.P. (2022). Identifying process-structure-property correlations related to the development of stress in metal thin films by high-throughput characterization and simulation-based methods [Conference Poster]. https://doi.org/10.2172/2005990 Publication ID: 120696
  • D’Elia, M., Bochev, P., Foster, J.T., Glusa, C., Gulian, M., Gunzburger, M., Trageser, J., Kuhlman, K.L., Martinez, M., Najm, H.N., Silling, S.A., Tupek, M., Xu, X., & Xu, X. (2022). Mathematical Foundations for Nonlocal Interface Problems: Multiscale Simulations of Heterogeneous Materials (Final LDRD Report). https://doi.org/10.2172/1888162 Publication ID: 80232
  • Williams, L., Sargsyan, K., Johnston, K., Najm, H.N., & Najm, H.N. (2022). Active Learning of SNAP Potentials using Bayesian Uncertainty Estimation [Conference Presenation]. https://doi.org/10.2172/2005291 Publication ID: 118616
  • Hudson, J., Sargsyan, K., D’Elia, M., Najm, H.N., & Najm, H.N. (2022). Examining stiffness in ResNets through interpretation as discretized Neural ODEs [Conference Presenation]. https://doi.org/10.2172/2004786 Publication ID: 117972
  • Safta, C., Sparapany, M., Grant, M.J., Najm, H.N., & Najm, H.N. (2022). Trajectory design via unsupervised probabilistic learning on optimal manifolds. Data-Centric Engineering, 3(2). https://doi.org/10.1017/dce.2022.26 Publication ID: 80143
  • Fan, Y., D’Elia, M., Yu, Y., Silling, S.A., Najm, H.N., & Najm, H.N. (2022). Bayesian Nonlocal Operator Regression (BNOR): Towards the Characterization of Uncertainty in Heterogeneous Materials [Conference Presenation]. https://doi.org/10.2172/2004482 Publication ID: 117116
  • Johnson, M., Gierada, M., Bross, D.H., Hermes, E.D., Blais, C., Goldsmith, C.F., West, R.H., Sargsyan, K., Najm, H.N., Zador, J., & Zador, J. (2022). pynta: An automated workflow code for reaction path exploration on surfaces [Conference Presenation]. https://doi.org/10.2172/2004380 Publication ID: 116736
  • Sargsyan, K., Williams, L., Najm, H.N., & Najm, H.N. (2022). Uncertainty Quantification of Machine Learning Interatomic Potential Models [Conference Poster]. https://doi.org/10.2172/2004338 Publication ID: 116568
  • Najm, H.N. (2022). Uncertainty Quantification and Active Learning in Atomistic Computations [Conference Presenation]. https://doi.org/10.2172/2003862 Publication ID: 114732
  • Robbe, P., Bonnet, L.A.P.L., Casey, T., Sargsyan, K., Najm, H.N., Matthews, C., Copper, M., Andersson, D., & Andersson, D. (2022). Bayesian calibration for summary statistics with applications to a cluster dynamics model [Conference Presenation]. https://doi.org/10.2172/2003860 Publication ID: 114724
  • Hansen, N., Taatjes, C.A., Zador, J., Chen, J., Najm, H.N., Sheps, L., Osborn, D.L., Ramasesha, K., & Ramasesha, K. (2022). Chemical Kinetics at SNL [Conference Presenation]. https://doi.org/10.2172/2003528 Publication ID: 113428
  • Robbe, P., Casey, T., Sargsyan, K., Bonnet, L.A.P.L., Najm, H.N., & Najm, H.N. (2022). Bayesian calibration of a cluster dynamics model [Conference Presenation]. https://doi.org/10.2172/2003389 Publication ID: 112888
  • Robbe, P., Casey, T., Sargsyan, K., Najm, H.N., & Najm, H.N. (2022). Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions [Conference Presenation]. https://doi.org/10.2172/2003181 Publication ID: 112128
  • Yang, Y., Najm, H.N., Zador, J., Eldred, M., & Eldred, M. (2022). A Neural Network Based Model for Reactive Heavy Hydrocarbon Potential Energy Surfaces [Conference Presenation]. https://doi.org/10.2172/2003159 Publication ID: 112040
  • Diaz-Ibarra, O.H., Kim, K., Safta, C., Najm, H.N., & Najm, H.N. (2022). Studying the Computational Performance of CSP Analysis on Heterogeneous Computing Platforms [Conference Presenation]. https://doi.org/10.2172/2003088 Publication ID: 111776
  • Oreluk, J., Sheps, L., Najm, H.N., & Najm, H.N. (2022). Bayesian Optimal Experimental Design for Photoionization Mass Spectrometry Experiments [Conference Presenation]. https://doi.org/10.2172/2003048 Publication ID: 111648
  • Younkin, T.R., Sargsyan, K., Casey, T., Najm, H.N., Canik, J.M., Green, D.L., Doerner, R.P., Nishijima, D., Baldwin, M., Drobny, J., Curreli, D., Wirth, B.D., & Wirth, B.D. (2022). Quantification of the effect of uncertainty on impurity migration in PISCES-A simulated with GITR. Nuclear Fusion, 62(5). https://doi.org/10.1088/1741-4326/ac2bfa Publication ID: 80875
  • Oreluk, J., Sheps, L., Najm, H.N., & Najm, H.N. (2022). Exploring Bayesian Optimal Experimental Designs for High-dimensional Combustion Systems [Conference Presenation]. https://doi.org/10.2172/2002385 Publication ID: 110472
  • Robbe, P., Casey, T., Sargsyan, K., Najm, H.N., & Najm, H.N. (2022). Uncertainty Quantification in Computational Modeling of Plasma-Surface Interactions [Conference Presenation]. https://doi.org/10.2172/2002352 Publication ID: 110352
  • Sargsyan, K., Williams, L., Johnston, K., Najm, H.N., & Najm, H.N. (2022). Quantification and Propagation of Uncertainties in Machine Learning Interatomic Potentials for Molecular Dynamics [Conference Presenation]. https://doi.org/10.2172/2002297 Publication ID: 110140
  • Hegde, A., Safta, C., Najm, H.N., Windl, W., Weiss, E., & Weiss, E. (2022). Bayesian Calibration of Interatomic Potential Models for Binary Alloys [Conference Presenation]. https://doi.org/10.2172/2002293 Publication ID: 110124
  • Diaz-Ibarra, O.H., Kim, K., Safta, C., Zador, J., Najm, H.N., & Najm, H.N. (2022). Using computational singular perturbation as a diagnostic tool in ODE and DAE systems: a case study in heterogeneous catalysis. Combustion Theory and Modelling, 26(2), pp. 201-227. https://doi.org/10.1080/13647830.2021.2002417 Publication ID: 76564
  • Hudson, J., Sargsyan, K., D’Elia, M., Najm, H.N., & Najm, H.N. (2021). Detecting stiffness in ResNets inspired by Neural ODEs [Conference Poster]. https://doi.org/10.2172/2001529 Publication ID: 107232
  • Najm, H.N., Yang, Y., Zador, J., Eldred, M., & Eldred, M. (2021). Surrogate models and physics constraints in atomistic modeling [Conference Presenation]. https://doi.org/10.2172/1903685 Publication ID: 77137
  • Diaz-Ibarra, O.H., Kim, K., Safta, C., Najm, H.N., & Najm, H.N. (2021). CSPlib – A Toolkit for the Analysis of ODE/DAE Dynamical Systems and Chemical Kinetic Models. https://doi.org/10.2172/1855067 Publication ID: 77175
  • Safta, C., Najm, H.N., Diaz-Ibarra, O.H., Kim, K., & Kim, K. (2021). TChem v3.0: A Software Toolkit for the Analysis of Complex Kinetic Models. https://doi.org/10.2172/1829197 Publication ID: 76644
  • Najm, H.N. (2021). Machine Learning in Computational Science [Conference Presenation]. https://doi.org/10.2172/1899485 Publication ID: 76591
  • Hermes, E., Blondal, K., Kreitz, B., Sargsyan, K., Najm, H.N., Zador, J., Goldsmith, C.F., West, R., & West, R. (2021). Application of a Novel Saddle Point Optimization Algorithm on Surface Reactions Involving Bidentate Adsorbates [Conference Poster]. https://doi.org/10.2172/1890874 Publication ID: 76044
  • Najm, H.N., Sargsyan, K., Kim, K., Goldsmith, C.F., West, R.H., Bylaska, E.J., Bross, D.H., Ruscic, B., Safta, C., Zador, J., Blais, C., Blondal, K., Diaz-Ibarra, O.H., Hermes, E., Mazeau, E., & Mazeau, E. (2021). Exascale Catalytic Chemistry (ECC) [Conference Poster]. https://doi.org/10.2172/1890393 Publication ID: 76037
  • Hermes, E., Sargsyan, K., Najm, H.N., Zador, J., & Zador, J. (2021). Geometry optimization speedup through a geodesic approach to internal coordinates. Journal of Chemical Physics, 155(9). https://doi.org/10.1063/5.0060146 Publication ID: 75381
  • Christopher, B., Diaz-Ibarra, O.H., Mazeau, E., Gierada, M., Hermes, E., Safta, C., Kim, K., Najm, H.N., Bylaska, E.J., Zador, J., Goldsmith, C.F., West, R.H., & West, R.H. (2021). AUTOMATIC GENERATION AND ANALYSIS OF MICROKINETIC MODELS [Conference Presenation]. https://doi.org/10.2172/1890361 Publication ID: 75926
  • Sargsyan, K., Johnstone, K., Dantanarayana, V., Najm, H.N., & Najm, H.N. (2021). Active Learning and Uncertainty Quantification for Machine Learning Interatomic Potentials [Conference Presenation]. https://doi.org/10.2172/1890912 Publication ID: 75904
  • Safta, C., Najm, H.N., Grant, M.J., Sparapany, M., & Sparapany, M. (2021). Trajectory Optimization via Unsupervised Probabilistic Learning On Manifolds. https://doi.org/10.2172/1821958 Publication ID: 75865
  • Najm, H.N., Yang, Y., & Yang, Y. (2021). AEVmod – Atomic Environment Vector Module Documentation. https://doi.org/10.2172/1817835 Publication ID: 79691
  • Hegde, A., Safta, C., Najm, H.N., Weiss, E., Windl, W., & Windl, W. (2021). Bayesian Calibration of Interatomic Potential Models for Binary Alloys [Conference Presenation]. https://doi.org/10.2172/1879292 Publication ID: 79538
  • Hudson, J., Sargsyan, K., D’Elia, M., Najm, H.N., & Najm, H.N. (2021). Analysis of Neural Networks as Dynamical Systems [Conference Presenation]. https://doi.org/10.2172/1883507 Publication ID: 79301
  • Hegde, A., Safta, C., Najm, H.N., Weiss, E., Windl, W., & Windl, W. (2021). Bayesian calibration of interatomic potential models for binary alloys [Conference Presenation]. https://doi.org/10.2172/1875384 Publication ID: 79051
  • Hegde, A., Safta, C., Najm, H.N., Weiss, E., Windl, W., & Windl, W. (2021). Bayesian calibration of interatomic potential models for binary alloys [Conference Presenation]. https://doi.org/10.2172/1877337 Publication ID: 78796
  • Safta, C., Diaz-Ibarra, O.H., Kim, K., Najm, H.N., & Najm, H.N. (2021). TChem ? An Open Source Computational Chemistry Software Library for Heterogeneous Computing Platforms [Conference Presenation]. https://doi.org/10.2172/1881665 Publication ID: 78610
  • Goussis, D.A., Im, H.G., Najm, H.N., Paolucci, S., Valorani, M., & Valorani, M. (2021). The origin of CEMA and its relation to CSP. Combustion and Flame, 227, pp. 396-401. https://doi.org/10.1016/j.combustflame.2021.01.020 Publication ID: 74417
  • Najm, H.N. (2021). Some Comments on Bayesian Optimal Experimental Design [Presentation]. https://www.osti.gov/biblio/1882502 Publication ID: 78045
  • Safta, C., Diaz-Ibarra, O.H., Najm, H.N., Kim, K., & Kim, K. (2021). TChem – An Open Source Computational Chemistry Software Library for Heterogenous Computing Platforms [Conference Paper]. https://www.osti.gov/biblio/1862775 Publication ID: 77992
  • Oreluk, J., Sheps, L., Najm, H.N., & Najm, H.N. (2021). Bayesian Optimal Experimental Design for Chemical Rate Constant Measurement using Mass Spectrometry [Conference Presenation]. https://doi.org/10.2172/1855329 Publication ID: 77548
  • Diaz-Ibarra, O.H., Kim, K., Safta, C., Najm, H.N., & Najm, H.N. (2021). CSPlib – A Software Toolkit for the Analysis of Dynamical Systems and Chemical Kinetic Models. https://doi.org/10.2172/1810242 Publication ID: 75971
  • Blondal, K., Sargsyan, K., Bross, D.H., Hermes, E., Najm, H.N., Zador, J., Goldsmith, C.F., & Goldsmith, C.F. (2020). Including anharmonicity in adsorbate partition functions and how it affects theoretical parameter estimates for desorption of methanol from a Cu(111) surface [Conference Presenation]. https://doi.org/10.2172/1831569 Publication ID: 71767
  • Safta, C., Kim, K., Diaz-Ibarra, O.H., Najm, H.N., & Najm, H.N. (2020). TChem v2.0 – A Software Toolkit for the Analysis of Complex Kinetic Models. https://doi.org/10.2172/1688569 Publication ID: 100660
  • Najm, H.N. (2020). Uncertainty Quantification in Large Scale Computational Models [Conference Poster]. https://www.osti.gov/biblio/1822295 Publication ID: 70930
  • Safta, C., Khalil, M., Najm, H.N., & Najm, H.N. (2020). Transitional Markov Chain Monte Carlo Sampler in UQTk. https://doi.org/10.2172/1606084 Publication ID: 105664
  • Safta, C., Ghanem, R.G., Huan, X., Lacaze, G., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2019). Design optimization of a scramjet under uncertainty using probabilistic learning on manifolds. Journal of Computational Physics, 399. https://doi.org/10.1016/j.jcp.2019.108930 Publication ID: 103180
  • Han, X., Najm, H.N., & Najm, H.N. (2019). Effective construction of eigenvectors for a class of singular sparse matrices. Applied Mathematics Letters, 97(C), pp. 121-126. https://doi.org/10.1016/j.aml.2019.05.018 Publication ID: 67071
  • Casey, T., Sargsyan, K., Najm, H.N., & Najm, H.N. (2019). Uncertainty quantification workflows in fusion plasma surface interaction modeling [Conference Poster]. https://www.osti.gov/biblio/1643062 Publication ID: 66036
  • Najm, H.N. (2019). Uncertainty Quantification with Model Error [Presentation]. https://www.osti.gov/biblio/1646081 Publication ID: 65732
  • Najm, H.N. (2019). Uncertainty Quantification in Large Scale Computational Models [Presentation]. https://www.osti.gov/biblio/1646080 Publication ID: 65731
  • Rai, P., Sargsyan, K., Najm, H.N., Hirata, S., & Hirata, S. (2019). Sparse low rank approximation of potential energy surfaces with applications in estimation of anharmonic zero point energies and frequencies. Journal of Mathematical Chemistry, 57(7), pp. 1732-1754. https://doi.org/10.1007/s10910-019-01034-z Publication ID: 104296
  • Najm, H.N., Safta, C., Huan, X., Casey, T., Sargsyan, K., Oefelein, J., Lacaze, G., Vane, Z., Eldred, M., Geraci, G., & Geraci, G. (2019). Uncertainty Quantification in Computational Models of Large Scale Physical Systems [Conference Poster]. https://www.osti.gov/biblio/1641505 Publication ID: 70287
  • Safta, C., Soize, C., Ghanem, R., Huan, X., Vane, Z.P., Oefelein, J., Lacaze, G., Najm, H.N., Tang, Q., Chen, X., & Chen, X. (2019). Entropy-based closure for probabilistic learning on manifolds. Journal of Computational Physics, 388(C), pp. 518-533. https://doi.org/10.1016/j.jcp.2018.12.029 Publication ID: 103916
  • Gormezano, C., Casey, T., Najm, H.N., & Najm, H.N. (2019). Distance Functions for Chaotic Dynamical Systems [Conference Poster]. https://www.osti.gov/biblio/1641188 Publication ID: 69842
  • Najm, H.N., Safta, C., Huan, X., Casey, T., Sargsyan, K., Oefelein, J., Lacaze, G., Eldred, M., Geraci, G., & Geraci, G. (2019). Uncertainty Quantification in Large Scale Computational Models [Conference Poster]. https://www.osti.gov/biblio/1641089 Publication ID: 69622
  • Casey, T., Debusschere, B.J., Eldred, M., Geraci, G., Ghanem, R., Jakeman, J.D., Marzouk, Y., Najm, H.N., Safta, C., Sargsyan, K., & Sargsyan, K. (2019). FASTMath: UQ Algorithms [Conference Poster]. https://www.osti.gov/biblio/1641088 Publication ID: 69621
  • Najm, H.N. (2019). Uncertainty Quantification in Large Scale Computational Models [Conference Poster]. https://www.osti.gov/biblio/1640740 Publication ID: 69103
  • Sargsyan, K., Younkin, T., Casey, T., Najm, H.N., Wirth, B., & Wirth, B. (2019). Uncertainty Quantification and Propagation for Impurity Migration [Conference Poster]. https://www.osti.gov/biblio/1643670 Publication ID: 68990
  • Najm, H.N. (2019). Explicit Time Integration of the Stiff Chemical Langevin Equation [Conference Poster]. https://www.osti.gov/biblio/1640628 Publication ID: 68978
  • Han, X., Valorani, M., Najm, H.N., & Najm, H.N. (2019). Explicit time integration of the stiff chemical Langevin equations using computational singular perturbation. Journal of Chemical Physics, 150(19). https://doi.org/10.1063/1.5093207 Publication ID: 67046
  • Najm, H.N. (2019). Surrogate modeling and uncertainty quantification in models of physical systems [Conference Poster]. https://www.osti.gov/biblio/1640180 Publication ID: 68793
  • Najm, H.N., Safta, C., & Safta, C. (2019). EGSim – a C++ Toolkit for Analysis of Power Grid Systems. https://doi.org/10.2172/1762344 Publication ID: 102248
  • Najm, H.N., Han, X., Valorani, M., & Valorani, M. (2019). Explicit Time Integration of Stiff Chemical Langevin Equations using Computational Singular Perturbation [Presentation]. https://www.osti.gov/biblio/1644785 Publication ID: 67796
  • Safta, C., Tsilifis, P., Huan, X., Sargsyan, K., Lacaze, G., Oefelein, J.C., Najm, H.N., Ghanem, R.G., & Ghanem, R.G. (2019). Compressive sensing adaptation for polynomial chaos expansions. Journal of Computational Physics, 380(C), pp. 29-47. https://doi.org/10.1016/j.jcp.2018.12.010 Publication ID: 58677
  • Huan, X., Sargsyan, K., Najm, H.N., & Najm, H.N. (2019). Embedded Model Error Representation for Bayesian Model Calibration. arXiv.org Repository, 2019, pp. 1-33. https://www.osti.gov/biblio/1529284 Publication ID: 58730
  • Sargsyan, K., Huan, X., Najm, H.N., & Najm, H.N. (2019). Embedded model error representation for bayesianmodel calibration. International Journal for Uncertainty Quantification, 9(4), pp. 365-394. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85073061599&origin=inward Publication ID: 104312
  • Huan, X., Safta, C., Vane, Z.P., Lacaze, G., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2019). Uncertainty propagation using conditional random fields in large-eddy simulations of scramjet computations [Conference Poster]. AIAA Scitech 2019 Forum. https://doi.org/10.2514/6.2019-0724 Publication ID: 71172
  • Geraci, G., Menhorn, F., Huan, X., Safta, C., Marzouk, Y.M., Najm, H.N., Eldred, M., & Eldred, M. (2019). Progress in scramjet design optimization under uncertainty using simulations of the HIFiRE direct connect rig [Conference Poster]. AIAA Scitech 2019 Forum. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85083943476&origin=inward Publication ID: 64163
  • Casey, T., Najm, H.N., & Najm, H.N. (2019). Estimating the joint distribution of rate parameters across multiple reactions in the absence of experimental data. Proceedings of the Combustion Institute, 37(1), pp. 797-805. https://doi.org/10.1016/j.proci.2018.06.190 Publication ID: 62881
  • Safta, C., Soize, C., Ghanem, R., Huan, X., Vane, Z.P., Oefelein, J.C., Lacaze, G., Najm, H.N., & Najm, H.N. (2019). Enhancing model predictability for a scramjet using probabilistic learning on manifolds. AIAA Journal, 57(1), pp. 365-378. https://doi.org/10.2514/1.j057069 Publication ID: 58678
  • Safta, C., Najm, H.N., & Najm, H.N. (2018). Enhancing statistical moment calculations for stochastic Galerkin solutions with Monte Carlo techniques. Journal of Computational Physics, 374(C), pp. 1017-1030. https://doi.org/10.1016/j.jcp.2018.07.004 Publication ID: 64063
  • Safta, C., Najm, H.N., Huan, X., Oefelein, J., & Oefelein, J. (2018). Uncertainty Propagation Using Conditional Random Fields in Large-Eddy Simulations of Scramjet Computations [Conference Poster]. https://www.osti.gov/biblio/1581581 Publication ID: 60670
  • Najm, H.N., Ghanem, R.G., Eldred, M., & Eldred, M. (2018). Design Optimization under Uncertainty in Large Scale Computational Models [Conference Poster]. https://www.osti.gov/biblio/1594692 Publication ID: 60189
  • Safta, C., Geraci, G., Eldred, M., Najm, H.N., Riegner, D., Windl, W., & Windl, W. (2018). Interatomic Potentials Models for Cu-Ni and Cu-Zr Alloys. https://doi.org/10.2172/1475252 Publication ID: 59193
  • Najm, H.N. (2018). Uncertainty Quantification with model Error [Conference Poster]. https://www.osti.gov/biblio/1593577 Publication ID: 59657
  • Najm, H.N. (2018). Uncertainty Quantification with Missing Data [Conference Poster]. https://www.osti.gov/biblio/1593576 Publication ID: 59656
  • Najm, H.N. (2018). Uncertainty Quantification in Computational Models of Large Scale Physical Systems [Conference Poster]. https://www.osti.gov/biblio/1593073 Publication ID: 59652
  • Khalil, M., Najm, H.N., & Najm, H.N. (2018). Probabilistic inference of reaction rate parameters from summary statistics. Combustion Theory and Modelling, 22(4), pp. 635-665. https://doi.org/10.1080/13647830.2017.1370557 Publication ID: 55233
  • Najm, H.N. (2018). Estimating the joint distribution of rate parameters across multiple reactions in the absence of experimental data [Conference Poster]. https://www.osti.gov/biblio/1806649 Publication ID: 63297
  • Najm, H.N. (2018). Enabling uncertainty quantification and propagation in combustion models using statistical inference of unreported experimental data [Conference Poster]. https://www.osti.gov/biblio/1806648 Publication ID: 63296
  • Sargsyan, K., Huan, X., Najm, H.N., & Najm, H.N. (2018). Bayesian Framework for Embedded Model Error Representation and Quantification [Conference Poster]. https://www.osti.gov/biblio/1806614 Publication ID: 63235
  • Safta, C., Huan, X., Najm, H.N., Sargsyan, K., Eldred, M., Geraci, G., & Geraci, G. (2018). Adaptive Sparse Quadrature for Multifidelity Scramjet Simulations [Conference Poster]. https://www.osti.gov/biblio/1569685 Publication ID: 63078
  • Huan, X., Safta, C., Sargsyan, K., Vane, Z.P., Lacaze, G., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2018). Compressive Sensing with Cross-Validation and Stop-Sampling for Sparse Polynomial Chaos Expansions. SIAM/ASA Journal on Uncertainty Quantification, 6(2), pp. 1-29. https://doi.org/10.1137/17M1141096 Publication ID: 98340
  • Safta, C., Cheng, J., Najm, H.N., Pinar, A., Chen, R.L.Y., Watson, J., & Watson, J. (2018). Chance-constrained economic dispatch with renewable energy and storage. Computational Optimization and Applications, 70(2), pp. 479-502. https://doi.org/10.1007/s10589-018-0006-2 Publication ID: 45606
  • Huan, X., Sargsyan, K., Najm, H.N., & Najm, H.N. (2018). Choosing Embedding for Capturing Model Misspecification [Conference Poster]. https://www.osti.gov/biblio/1529749 Publication ID: 62692
  • Hermes, E., Rai, P., Sargsyan, K., Zador, J., Najm, H.N., & Najm, H.N. (2018). Optimization of low-rank tensor functional approximations [Conference Poster]. https://www.osti.gov/biblio/1525693 Publication ID: 62439
  • Casey, T., Najm, H.N., & Najm, H.N. (2018). Parameter estimation for system submodels with limited or missing data using a data-free inference procedure [Conference Poster]. https://www.osti.gov/biblio/1515742 Publication ID: 62181
  • Safta, C., Huan, X., Sargsyan, K., Najm, H.N., Eldred, M., Geraci, G., & Geraci, G. (2018). Sparse multifidelity approximations for forward UQ [Conference Poster]. https://www.osti.gov/biblio/1510682 Publication ID: 61747
  • Najm, H.N. (2018). Accounting for model Error in UQ [Conference Poster]. https://www.osti.gov/biblio/1508915 Publication ID: 61650
  • Najm, H.N. (2018). Advanced Topics in UQ [Conference Poster]. https://www.osti.gov/biblio/1508914 Publication ID: 61649
  • Najm, H.N. (2018). Forward Propagation of Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1508913 Publication ID: 61648
  • Najm, H.N. (2018). Statistical Inverse Problems and Bayesian Inference [Conference Poster]. https://www.osti.gov/biblio/1508912 Publication ID: 61647
  • Najm, H.N. (2018). Foundations of Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1508911 Publication ID: 61646
  • Rai, P., Sargsyan, K., Najm, H.N., & Najm, H.N. (2018). Compressed sparse tensor based quadrature for vibrational quantum mechanics integrals. Computer Methods in Applied Mechanics and Engineering, 336, pp. 1-27. https://doi.org/10.1016/j.cma.2018.02.026 Publication ID: 61090
  • Najm, H.N. (2018). Uncertainty Quantification in Computational Models [Conference Poster]. https://www.osti.gov/biblio/1503759 Publication ID: 61251
  • Huan, X., Safta, C., Sargsyan, K., Geraci, G., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J., Najm, H.N., & Najm, H.N. (2018). Global Sensitivity Analysis and Estimation of Model Error, Toward Uncertainty Quantification in Scramjet Computations. AIAA Journal, 56(3), pp. 1170-1184. https://doi.org/10.2514/1.J056278 Publication ID: 60730
  • Najm, H.N., Debusschere, B.J., Sparks, N., Sargsyan, K., Huan, X., Oefelein, J., Vane, Z., Eldred, M., Geraci, G., Knio, O., Sraj, I., Scovazzi, G., Colomes, O., Marzouk, Y., Zahm, O., Menhorn, F., Ghanem, R., Tsilifis, P., & Tsilifis, P. (2018). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ? [Conference Poster]. https://www.osti.gov/biblio/1572447 Publication ID: 60908
  • Debusschere, B.J., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., Najm, H.N., & Najm, H.N. (2018). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1513665 Publication ID: 58794
  • Huan, X., Safta, C., Sargsyan, K., Geraci, G., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2018). Global sensitivity analysis and estimation of model error, toward uncertainty quantification in scramjet computations. AIAA Journal, 56(3), pp. 1170-1184. https://doi.org/10.2514/1.J056278 Publication ID: 57736
  • Huan, X., Geraci, G., Vane, Z.P., Safta, C., Eldred, M., Oefelein, J., Najm, H.N., Sargsyan, K., & Sargsyan, K. (2018). Multifidelity Statistical Analysis of Large Eddy Simulations in Scramjet Computations [Conference Poster]. https://doi.org/10.2514/6.2018-1180 Publication ID: 58547
  • Debusschere, B.J., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., Najm, H.N., & Najm, H.N. (2018). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1512381 Publication ID: 58575
  • Huan, X., Geraci, G., Safta, C., Eldred, M., Sargsyan, K., Vane, Z.P., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2018). Multifidelity statistical analysis of large eddy simulations in scramjet computations [Conference Poster]. AIAA Non-Deterministic Approaches Conference, 2018. https://doi.org/10.2514/6.2018-1180 Publication ID: 58602
  • Safta, C., Huan, X., Geraci, G., Eldred, M., Sargsyan, K., Vane, Z.P., Oefelein, J., Najm, H.N., & Najm, H.N. (2018). Multifidelity Statistical Analysis of Large Eddy Simulations in Scramjet Computations [Conference Poster]. https://www.osti.gov/biblio/1513486 Publication ID: 58676
  • Huan, X., Safta, C., Sargsyan, K., Vane, Z.P., Lacaze, G., Oefelein, J.C., Najm, H.N., & Najm, H.N. (2018). Compressive sensing with cross-validation and stop-sampling for sparse polynomial chaos expansions [Conference Poster]. SIAM-ASA Journal on Uncertainty Quantification. https://doi.org/10.1137/17M1141096 Publication ID: 61717
  • Sargsyan, K., Safta, C., Ricciuto, D., Thornton, P., Najm, H.N., Huan, X., & Huan, X. (2017). Embedded Model Error Representation and Propagation in Climate Models [Conference Poster]. https://www.osti.gov/biblio/1512030 Publication ID: 54686
  • Casey, T., Najm, H.N., & Najm, H.N. (2017). Missing experimental data and rate parameter inference for H2+OH=H2O+H [Conference Poster]. https://www.osti.gov/biblio/1479242 Publication ID: 53752
  • Rai, P., Sargsyan, K., Najm, H.N., Hermes, M.R., Hirata, S., & Hirata, S. (2017). Low-rank canonical-tensor decomposition of potential energy surfaces: application to grid-based diagrammatic vibrational Green’s function theory. Molecular Physics, 115(17-18), pp. 2120-2134. https://doi.org/10.1080/00268976.2017.1288937 Publication ID: 52923
  • Debusschere, B.J., Rizzi, F., Bachman, W.B., Sargsyan, K., Safta, C., Najm, H.N., Knio, O., Mycek, P., Contreras, A., le Olivier, M., & le Olivier, M. (2017). Probabilistic Approach to Enable Extreme-Scale Simulations under Uncertainty and System Faults [Conference Poster]. https://www.osti.gov/biblio/1470926 Publication ID: 58435
  • Najm, H.N. (2017). Some Comments on the Future of Uncertainty Quantification Research [Conference Poster]. https://www.osti.gov/biblio/1470683 Publication ID: 58365
  • Najm, H.N., Debusschere, B.J., Safta, C., Sargsyan, K., Huan, X., Oefelein, J., Vane, Z., Eldred, M., Geraci, G., Knio, O., Sraj, I., Scovazzi, G., Colomes, O., Marzouk, Y., Zahm, O., Menhorn, F., Ghanem, R., Tsilifis, P., & Tsilifis, P. (2017). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine [Conference Poster]. https://www.osti.gov/biblio/1574094 Publication ID: 58225
  • Debusschere, B.J., Pinar, A., Sargsyan, K., Templeton, J.A., Najm, H.N., & Najm, H.N. (2017). Predictive Fidelity Interpretability and Resilience of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1508933 Publication ID: 58088
  • Najm, H.N. (2017). An Introduction to Statistical Inverse Problems and Bayesian Inference [Conference Poster]. https://www.osti.gov/biblio/1465087 Publication ID: 57997
  • Najm, H.N., Casey, T., Khalil, M., & Khalil, M. (2017). Parameter Estimation in Chemical Systems [Conference Poster]. https://www.osti.gov/biblio/1465086 Publication ID: 57996
  • Huan, X., Sargsyan, K., Najm, H.N., & Najm, H.N. (2017). Bayesian Model Calibration with an Embedded Statistical Characterization of Model Error [Conference Poster]. https://www.osti.gov/biblio/1462643 Publication ID: 57597
  • Huan, X., Sargsyan, K., Najm, H.N., & Najm, H.N. (2017). A Non-Intrusive Embedding Approach for Statistical Characterization of Model Error [Conference Poster]. https://www.osti.gov/biblio/1462642 Publication ID: 57596
  • Najm, H.N., Sargsyan, K., Huan, X., Khalil, M., Hakim, L., Oefelein, J., Lacaze, G., Vane, Z.P., & Vane, Z.P. (2017). Bayesian Estimation of Model Error in Physical Systems [Conference Poster]. https://www.osti.gov/biblio/1462640 Publication ID: 57594
  • Lao, J., Safta, C., Najm, H.N., & Najm, H.N. (2017). Sampling Complex Distributions with Transitional Markov Chain Monte Carlo [Presentation]. https://www.osti.gov/biblio/1507507 Publication ID: 57441
  • Safta, C., Chen, R.L.Y., Najm, H.N., Pinar, A., Watson, J., & Watson, J. (2017). Efficient Uncertainty Quantification in Stochastic Economic Dispatch. IEEE Transactions on Power Systems, 32(4), pp. 2535-2546. https://doi.org/10.1109/TPWRS.2016.2615334 Publication ID: 99156
  • Najm, H.N., Casey, T., Khalil, M., & Khalil, M. (2017). Statistical Inference given Summary Statistics in Chemical Models [Conference Poster]. https://www.osti.gov/biblio/1506217 Publication ID: 57004
  • Wang, L., Han, X., Cao, Y., Najm, H.N., & Najm, H.N. (2017). Computational singular perturbation analysis of stochastic chemical systems with stiffness. Journal of Computational Physics, 335(C), pp. 404-425. https://doi.org/10.1016/j.jcp.2017.01.040 Publication ID: 52618
  • Huan, X., Sargsyan, K., Vane, Z.P., Lacaze, G., Oefelein, J., Najm, H.N., & Najm, H.N. (2017). Quantifying Uncertainty from Model Error in Turbulent Combustion Applications [Conference Poster]. https://www.osti.gov/biblio/1456433 Publication ID: 55632
  • Safta, C., Phipps, E.T., Najm, H.N., & Najm, H.N. (2017). Intrusive UQ Algorithms for Emerging Computing Platforms [Conference Poster]. https://www.osti.gov/biblio/1456382 Publication ID: 55561
  • Najm, H.N., Sargsyan, K., Huan, X., Hakim, L., Khalil, M., Oefelein, J., Lacaze, G., Vane, Z.P., & Vane, Z.P. (2017). Model Error and Statistical Calibration of [Conference Poster]. https://www.osti.gov/biblio/1426633 Publication ID: 55340
  • Huan, X., Safta, C., Geraci, G., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J., Sargsyan, K., Najm, H.N., & Najm, H.N. (2017). Robust Compressive Sensing with Application to Multifidelity Analysis of Complex Turbulent Flows [Conference Poster]. https://www.osti.gov/biblio/1427446 Publication ID: 55202
  • Safta, C., Blaylock, M., Templeton, J.A., Domino, S.P., Sargsyan, K., Najm, H.N., & Najm, H.N. (2017). Uncertainty quantification in LES of channel flow. International Journal for Numerical Methods in Fluids, 83(4), pp. 376-401. https://doi.org/10.1002/fld.4272 Publication ID: 41556
  • Najm, H.N. (2017). An Overview of the QUEST Institute [Conference Poster]. https://www.osti.gov/biblio/1455319 Publication ID: 54876
  • Casey, T., Khalil, M., Najm, H.N., & Najm, H.N. (2017). Inference of H2O2 thermal decomposition rate parameters from experimental statistics [Conference Poster]. 10th U.S. National Combustion Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040190506&origin=inward Publication ID: 56074
  • Casey, T., Khalil, M., Najm, H.N., & Najm, H.N. (2017). Inference of H2O2 thermal decomposition rate parameters from experimental statistics [Conference Poster]. 10th U.S. National Combustion Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040190506&origin=inward Publication ID: 55843
  • Casey, T., Najm, H.N., & Najm, H.N. (2017). Missing experimental data and rate parameter inference for H2+OH=H2O+H [Conference Poster]. 2017 Fall Technical Meeting of the Western States Section of the Combustion Institute, WSSCI 2017. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85040168373&origin=inward Publication ID: 53751
  • Huan, X., Safta, C., Sargsyan, K., Geraci, G., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J., Najm, H.N., & Najm, H.N. (2017). Global sensitivity analysis and quantification of model error for large eddy simulation in scramjet design [Conference Poster]. 19th AIAA Non-Deterministic Approaches Conference, 2017. https://doi.org/10.2514/6.2017-1089 Publication ID: 52795
  • Casey, T., Najm, H.N., & Najm, H.N. (2017). Inference and combination of missing data sets for investigation of H2O2 thermal decomposition rate uncertainty [Conference Poster]. 11th Asia-Pacific Conference on Combustion, ASPACC 2017. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85046675791&origin=inward Publication ID: 58611
  • Sargsyan, K., Safta, C., Najm, H.N., Debusschere, B.J., Ricciuto, D., Thornton, P., & Thornton, P. (2016). Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction [Conference Poster]. https://www.osti.gov/biblio/1576186 Publication ID: 48156
  • Huan, X., Safta, C., Sargsyan, K., Geraci, G., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J., Najm, H.N., & Najm, H.N. (2016). Global Sensitivity Analysis and Quantification of Model Form Error for Large Eddy Simulation of Scramjet Design [Conference Poster]. https://www.osti.gov/biblio/1413414 Publication ID: 48116
  • Najm, H.N., Debusschere, B.J., Safta, C., Sargsyan, K., Huan, X., Oefelein, J., Lacaze, G., Vane, Z.P., Eldred, M., Geraci, G., Knio, O., Sraj, I., Scovazzi, G., Colomes, O., Marzouk, Y., Zahm, O., Menhorn, F., Ghanem, R., Tsilifis, P., & Tsilifis, P. (2016). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ? [Conference Poster]. https://www.osti.gov/biblio/1420843 Publication ID: 47626
  • Khalil, M., Chowdhary, K., Safta, C., Sargsyan, K., Najm, H.N., & Najm, H.N. (2016). Inference of reaction rate parameters based on summary statistics from experiments. Proceedings of the Combustion Institute. https://doi.org/10.1016/j.proci.2016.08.058 Publication ID: 50088
  • Najm, H.N. (2016). An Introduction to Bayesian Inference [Conference Poster]. https://www.osti.gov/biblio/1397107 Publication ID: 52538
  • Najm, H.N. (2016). Chemical Model Reduction under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1527238 Publication ID: 52536
  • Najm, H.N., Debusschere, B.J., Safta, C., Sargsyan, K., Huan, X., Oefelein, J., Lacaze, G., Vane, Z.P., Eldred, M., Geraci, G., Knio, O., Sraj, I., Scovazzi, G., Colomes, O., Marzouk, Y., Zahm, O., Augustin, F., Menhorn, F., Ghanem, R., Tsilifis, P., & Tsilifis, P. (2016). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine [Conference Poster]. https://www.osti.gov/biblio/1397105 Publication ID: 52534
  • Najm, H.N., Sargsyan, K., Huan, X., Khalil, M., Hakim, L., Oefelein, J., Lacaze, G., Vane, Z.P., & Vane, Z.P. (2016). Uncertainty Quantification with Model Error [Conference Poster]. https://www.osti.gov/biblio/1397104 Publication ID: 52533
  • Najm, H.N., Sargsyan, K., Huan, X., Hakim, L., Oefelein, J., Lacaze, G., Vane, Z.P., & Vane, Z.P. (2016). Uncertainty Quantification with Model Error [Conference Poster]. https://www.osti.gov/biblio/1527218 Publication ID: 52156
  • Najm, H.N. (2016). Optimization under Uncertainty – Scramjet and Power Grid – [Conference Poster]. https://www.osti.gov/biblio/1389706 Publication ID: 52069
  • Chowdhary, K., Najm, H.N., & Najm, H.N. (2016). Bayesian estimation of Karhunen-Loève expansions; A random subspace approach. Journal of Computational Physics, 319(C), pp. 280-293. https://doi.org/10.1016/j.jcp.2016.02.056 Publication ID: 42319
  • Harmon, R., Khalil, M., Najm, H.N., Safta, C., & Safta, C. (2016). Convergence Study in Global Sensitivity Analysis. https://doi.org/10.2172/1561829 Publication ID: 51622
  • Khalil, M., Chowdhary, K., Safta, C., Sargsyan, K., Najm, H.N., & Najm, H.N. (2016). Inference of reaction rate parameters based on summary statistics from experiments [Conference Poster]. https://doi.org/10.1016/j.proci.2016.08.058 Publication ID: 51426
  • Najm, H.N., Sargsyan, K., Huan, X., Bender, J., Ghanem, R., & Ghanem, R. (2016). On Model Error and Statistical Calibration of Physical Models [Conference Poster]. https://www.osti.gov/biblio/1529794 Publication ID: 51268
  • Wang, J.M., Najm, H.N., Khalil, M., Freund, J., & Freund, J. (2016). Global Sensitivity Analysis for Fields: A Demonstration for Hydrogen Autoignition [Presentation]. https://www.osti.gov/biblio/1373231 Publication ID: 51267
  • Heindel, J., Rai, P., Jasper, A.W., Sargsyan, K., Najm, H.N., & Najm, H.N. (2016). Low-rank decomposition of potential energy surfaces for rovibrational properties at high energies [Presentation]. https://www.osti.gov/biblio/1373066 Publication ID: 51259
  • Harmon, R., Najm, H.N., Khalil, M., & Khalil, M. (2016). A Convergence Study in Global Sensitivity Analysis [Presentation]. https://www.osti.gov/biblio/1372191 Publication ID: 51112
  • Harmon, R., Najm, H.N., Khalil, M., & Khalil, M. (2016). A Convergence Study in Global Sensitivity Analysis (presentation) [Presentation]. https://www.osti.gov/biblio/1372190 Publication ID: 51111
  • Huan, X., Safta, C., Eldred, M., Vane, Z.P., Lacaze, G., Oefelein, J., Sargsyan, K., Najm, H.N., & Najm, H.N. (2016). Global Sensitivity Analysis for Large Eddy Simulation Models [Conference Poster]. https://www.osti.gov/biblio/1372012 Publication ID: 51034
  • Safta, C., Chen, R.L.Y., Najm, H.N., Pinar, A., Watson, J., & Watson, J. (2016). A Sparse Quadrature Approach for Stochastic Optimization in Power Grid Models [Conference Poster]. https://www.osti.gov/biblio/1367949 Publication ID: 50837
  • Najm, H.N. (2016). Reacting Flow Modeling with Detailed Chemical Kinetics [Conference Poster]. https://www.osti.gov/biblio/1877834 Publication ID: 50534
  • Sargsyan, K., Huan, X., Najm, H.N., Bender, J., & Bender, J. (2016). Density Estimation Framework for Model Error Quantification [Conference Poster]. https://www.osti.gov/biblio/1368714 Publication ID: 50301
  • Oefelein, J., Hakim, L., Lacaze, G., Khalil, M., Sargsyan, K., Najm, H.N., & Najm, H.N. (2016). Parameter Estimation and Uncertainty Quantification in Turbulent Combustion Computations [Conference Poster]. https://www.osti.gov/biblio/1366683 Publication ID: 49208
  • Sargsyan, K., Huan, X., Najm, H.N., & Najm, H.N. (2016). Density Estimation Framework for Model Error Quantification [Conference Poster]. https://www.osti.gov/biblio/1366667 Publication ID: 49200
  • Sargsyan, K., Ricciuto, D., Thornton, P., Safta, C., Debusschere, B.J., Najm, H.N., & Najm, H.N. (2016). Quantifying the Impacts of Parametric Uncertainty on Biogeochemistry in the ACME Land Model [Conference Poster]. https://www.osti.gov/biblio/1366666 Publication ID: 49199
  • Khalil, M., Najm, H.N., Chowdhary, K., Safta, C., Sargsyan, K., & Sargsyan, K. (2016). Probabilistic Inference of Model Parameters and Missing High-Dimensional Data Based on Summary Statistics [Conference Poster]. https://www.osti.gov/biblio/1618243 Publication ID: 49171
  • Safta, C., Cheng, J., Chen, R.L.Y., Pinar, A., Najm, H.N., Watson, J., & Watson, J. (2016). Surrogate-based model for optimization under uncertainty [Conference Poster]. https://www.osti.gov/biblio/1618234 Publication ID: 49142
  • Safta, C., Chen, R.L.Y., Pinar, A., Najm, H.N., Watson, J., & Watson, J. (2016). Employing Sparse Quadrature for Stochastic Optimization in Power Grids [Conference Poster]. https://www.osti.gov/biblio/1618230 Publication ID: 49135
  • Najm, H.N., Debusschere, B.J., Safta, C., Sargsyan, K., Oefelein, J., Lacaze, G., Eldred, M., Knio, O., Scovazzi, G., Marzouk, Y., Ghanem, R., & Ghanem, R. (2016). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine [Conference Poster]. https://www.osti.gov/biblio/1530652 Publication ID: 49101
  • Najm, H.N. (2016). Uncertainty Quantification in Computational Models [Conference Poster]. https://www.osti.gov/biblio/1346524 Publication ID: 48581
  • Debusschere, B.J., Najm, H.N., Sargsyan, K., Chowdhary, K., Lucas, D., Bulaevskaya, V., Qian, Y., Ghan, S., Rosa, D., Collins, B., & Collins, B. (2016). Calibration and Comparison of Climate Models: Accounting for Structural and Discretization Error [Poster] [Conference Poster]. https://www.osti.gov/biblio/1239394 Publication ID: 46817
  • Najm, H.N., Mapica Galassi, R.C., Valorani, M., & Valorani, M. (2015). Chemical Model Reduction under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1338156 Publication ID: 42116
  • Sargsyan, K., Safta, C., Najm, H.N., Debusschere, B.J., Ricciuto, D., Thornton, P., & Thornton, P. (2015). Sparse Polynomial Chaos Surrogate for ACME Land Model via Iterative Bayesian Compressive Sensing [Conference Poster]. https://www.osti.gov/biblio/1335739 Publication ID: 41907
  • Rizzi, F., Knio, O., Jones, R.E., Adalsteisson, H., Najm, H.N., Sargsyan, K., Salloum, M., Safta, C., Debusschere, B.J., & Debusschere, B.J. (2015). UQ in Molecular Dynamics Simulations: Forward and Inverse Problem [Presentation]. https://www.osti.gov/biblio/1335540 Publication ID: 41880
  • Debusschere, B.J., Najm, H.N., Sargsyan, K., Chowdhary, K., Lucas, D., Bulaevskaya, V., Qian, Y., Ghan, S., Rosa, D., Collins, W., & Collins, W. (2015). Calibration and Comparison of Climate Models: Accounting for Structural and Discretization Error [Conference Poster]. https://www.osti.gov/biblio/1338030 Publication ID: 41809
  • Najm, H.N. (2015). Optimization and Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1333804 Publication ID: 41749
  • Najm, H.N. (2015). Uncertainty Quantification in Computational Models [Presentation]. https://www.osti.gov/biblio/1332657 Publication ID: 46301
  • Templeton, J.A., Blaylock, M., Domino, S.P., Hewson, J.C., Kumar, P.R., Ling, J., Najm, H.N., Ruiz, A., Safta, C., Sargsyan, K., Stewart, A., Wagner, G., & Wagner, G. (2015). Calibration and Forward Uncertainty Propagation for Large-eddy Simulations of Engineering Flows. https://doi.org/10.2172/1221181 Publication ID: 45562
  • Rizzi, F., Bachman, W.B., Sargsyan, K., Mycek, P., Safta, C., Lemaitre, O., Knio, O., Najm, H.N., Debusschere, B.J., & Debusschere, B.J. (2015). Partial Differential Equations Solver Resilient to Soft and Hard Faults [Conference Poster]. https://www.osti.gov/biblio/1326569 Publication ID: 45304
  • Sargsyan, K., Rai, P., Najm, H.N., Hermes, M., Hirata, S., & Hirata, S. (2015). Low Rank Approximation-based Quadrature for Fast Evaluation of Multi-Particle Integrals. https://doi.org/10.2172/1221857 Publication ID: 45276
  • Najm, H.N., Chowdhary, K., & Chowdhary, K. (2015). Inference given Summary Statistics. https://www.osti.gov/biblio/1227182 Publication ID: 45191
  • Najm, H.N., Sargsyan, K., Chowdhary, K., Khalil, M., & Khalil, M. (2015). Computational Statistical Inverse Problems with Sparse or Missing Data [Conference Poster]. https://www.osti.gov/biblio/1312660 Publication ID: 45189
  • Safta, C., Chen, R.L.Y., Najm, H.N., Pinar, A., Watson, J., & Watson, J. (2015). An Efficient Approach for Stochastic Optimization of Electricity Grid Operations [Conference Poster]. https://www.osti.gov/biblio/1301962 Publication ID: 44869
  • Rai, P., Sargsyan, K., Najm, H.N., Hirata, S., Matthew, H., & Matthew, H. (2015). Low Rank Approximation-based Quadrature for Fast Evaluation of Multi-Particle Integrals [Conference Poster]. https://www.osti.gov/biblio/1266829 Publication ID: 44431
  • Chen, C., Najm, H.N., Khalil, M., & Khalil, M. (2015). Global Sensitivity Analysis for Chemical Kinetics of Hydrocarbon Combustion [Poster] [Presentation]. https://www.osti.gov/biblio/1339328 Publication ID: 44644
  • Chen, C., Najm, H.N., Khalil, M., & Khalil, M. (2015). Global Sensitivity Analysis for Chemical Kinetics of Hydrocarbon Combustion [PowerPoint] [Presentation]. https://www.osti.gov/biblio/1339331 Publication ID: 44647
  • Sargsyan, K., Najm, H.N., Jason, B., Ghanem, R., & Ghanem, R. (2015). Density Estimation Framework for Model Error Assessment [Conference Poster]. https://www.osti.gov/biblio/1279688 Publication ID: 44743
  • Najm, H.N., Valorani, M., Safta, C., Khalil, M., Ciottoli, P.P., Galassi, R.C., & Galassi, R.C. (2015). Chemical Model Reduction under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1268977 Publication ID: 44045
  • Salloum, M., Sargsyan, K., Jones, R.E., Rizzi, F., Najm, H.N., Debusschere, B.J., & Debusschere, B.J. (2015). Quantifying Sampling Noise and Parametric Uncertainty in Coupled Atomistic-Continuum Simulations [Conference Poster]. https://www.osti.gov/biblio/1258267 Publication ID: 43585
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