Moe Khalil

R&D S&E, Computer Science

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R&D S&E, Computer Science

mkhalil@sandia.gov

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Biography

Moe Khalil is a Principal Member of the Technical Staff at Sandia National Laboratories, Livermore, California, in the Quantitative Modeling and Software Engineering department. He also holds an adjunct research professor position in the department of Civil & Environmental Engineering at Carleton University, Canada.  He received a B.Sc. in Microbiology and Immunology and a B.Eng. in Computer and Electrical Engineering from McGill University, Canada, and M.Sc. and Ph.D. degrees in Civil and Environmental Engineering from Carleton University, Canada.

He has 20 years of experience developing Bayesian inference algorithms for machine-learning model calibration, parameter estimation, data assimilation, data-driven model selection, and transfer learning, with applications in fluid-structure interaction, material science, combustion modeling, radiation detection, nonlinear structural dynamics, wildfire forecasting, time-series analysis, and near-shore wave forecasting for energy harvesting.

Education

Ph.D.Civil & Environmental EngineeringCarleton University2013
M.A.Sc.Civil & Environmental EngineeringCarleton University2006
B.Eng.Electrical & Computer EngineeringMcGill University2004
B.Sc.Microbiology & ImmunologyMcGill University2000

Publications

  • Khalil, M., Balakrishnan, U., Jones, R.E., Safta, C., Chen, J., Wu, X., Huang, Y., & Huang, Y. (2022). Enhancing polynomial chaos expansion based surrogates through probabilistic transfer learning [Conference Presenation]. https://doi.org/10.2172/2005310 Publication ID: 118688
  • Swansen, E., Khalil, M., Hovakimyan, N., & Hovakimyan, N. (2022). Transfer Learning of Gaussian Processes to Capture Unmodeled Physics [Conference Poster]. https://doi.org/10.2172/2004711 Publication ID: 117676
  • Bridgman, W.H., Jones, R.E., Khalil, M., & Khalil, M. (2022). Robust initializations of variational inference using global optimization and Laplace approximations [Conference Presenation]. https://doi.org/10.2172/2004800 Publication ID: 118028
  • Jung, K., Echekki, T., Chen, J., Khalil, M., & Khalil, M. (2022). Transfer Learning of Closure Terms and in Reduced Order Models of Chemically Reactive Flows [Conference Presenation]. https://doi.org/10.2172/2005281 Publication ID: 118576
  • Swansen, E., Gahlawat, A., Hovakimyan, N., Khalil, M., & Khalil, M. (2022). Transfer learning of Gaussian processes to capture unmodeled physics: application to control of nonlinear dynamical systems [Conference Presenation]. https://doi.org/10.2172/2005304 Publication ID: 118664
  • Bridgman, W.H., Zhang, X., Teichert, G., Khalil, M., Garikipati, K., Bachman, W.B., & Bachman, W.B. (2022). A heteroencoder architecture for prediction of failure locations in porous metals using variational inference [Conference Presenation]. Computer Methods in Applied Mechanics and Engineering. https://doi.org/10.2172/2002242 Publication ID: 109932
  • Khalil, M., Bridgman, W.H., & Bridgman, W.H. (2022). Probabilistic Approaches to Transfer Learning [Conference Presenation]. https://doi.org/10.2172/2003967 Publication ID: 115140
  • Souza Soriano, B., Shimizu, Y., Rieth, M., Echeckki, T., Chen, J., Khalil, M., & Khalil, M. (2022). Coupling of NovelProbabilistic Transfer Learning Strategies and Autoencoders to Expedite Turbulent Combustion Modeling [Conference Presenation]. https://doi.org/10.2172/2003084 Publication ID: 111760
  • Khalil, M. (2022). Probabilistic Approaches to Transfer Learning for Sparse and Noisy Data Environments [Conference Presenation]. https://doi.org/10.2172/2002258 Publication ID: 109996
  • Bisaillon, P., Sandhu, R., Pettit, C., Khalil, M., Poirel, D., Manohar, C.S., Sarkar, A., & Sarkar, A. (2022). Combined selection of the dynamic model and modeling error in nonlinear aeroelastic systems using Bayesian Inference. Journal of Sound and Vibration, 522. https://doi.org/10.1016/j.jsv.2021.116418 Publication ID: 66015
  • Bridgman, W.H., Jones, R.E., Khalil, M., & Khalil, M. (2022). Robust initialization of variational inference through global optimization and Laplace approximations [Conference Poster]. https://doi.org/10.2172/2002160 Publication ID: 109608
  • Teichert, G.H., Khalil, M., Alleman, C., Garikipati, K., Jones, R.E., & Jones, R.E. (2022). Sensitivity of void mediated failure to geometric design features of porous metals [Conference Presenation]. International Journal of Solids and Structures. https://doi.org/10.2172/1882104 Publication ID: 79591
  • Souza Soriano, B., Shimizu, Y., Rieth, M., Khalil, M., Chen, J., Echekki, T., & Echekki, T. (2022). Coupling of Novel Probabilistic Transfer Learning Strategies and Autoencoders to Expedite Turbulent Combustion Modeling [Conference Presenation]. https://doi.org/10.2172/2006429 Publication ID: 122368
  • Sargsyan, K., Safta, C., Boll, L., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Zeng, X., Debusschere, B.J., & Debusschere, B.J. (2022). UQTk Version 3.1.2 User Manual. https://doi.org/10.2172/1855040 Publication ID: 79950
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Extending relevance vector machine to resolve overtting during Bayesian calibration in nonlinear stochastic dynamics [Conference Presenation]. https://doi.org/10.2172/1890899 Publication ID: 76095
  • Coe, R.G., Bacelli, G., Gaebele, D.T., Alfred, C., McNatt, C., Wilson, D.G., Weaver, J.L., Kasper, J.L., Khalil, M., Dallman, A., & Dallman, A. (2021). Modeling and predicting power from a WEC array [Conference Paper]. https://www.osti.gov/biblio/1894013 Publication ID: 76363
  • Khalil, M. (2021). Probabilistic Approaches to Transfer Learning [Conference Presenation]. https://doi.org/10.2172/1882483 Publication ID: 79375
  • Bisaillon, P., Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). A parallel update step of a sampling-free EnKF-type %0Clter [Conference Presenation]. https://doi.org/10.2172/1872702 Publication ID: 78805
  • Robinson, B., Bisaillon, P., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Nonlinear sparse Bayesian learning using EnKF based state estimator [Conference Presenation]. https://doi.org/10.2172/1872703 Publication ID: 78806
  • Khalil, M. (2021). Probabilistic Approaches to Transfer Learning [Presentation]. https://www.osti.gov/biblio/1861973 Publication ID: 77914
  • Chang, G., Dallman, A., Raghukumar, K., Khalil, M., Kasper, J., Jones, C., Roberts, J.D., & Roberts, J.D. (2021). Wave Energy Production Optimization and Forecasting Tool [Conference Poster]. https://doi.org/10.2172/1862634 Publication ID: 77952
  • Robinson, B., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Sparse learning of a nonlinear aeroelastic model using Bayesian inference [Conference Presenation]. https://doi.org/10.2172/1853863 Publication ID: 77418
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Sparse Learning of Over-Parametrized Nonlinear Engineering Models [Conference Presenation]. https://doi.org/10.2172/1853864 Publication ID: 77419
  • Khalil, M., Dallman, A., Raghukumar, K., Flanary, C., & Flanary, C. (2021). Wave Data Assimilation in Support of Wave Energy Converter Power Prediction [Conference Presenation]. https://doi.org/10.2172/1854077 Publication ID: 77441
  • Bisaillon, P., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Calibrating optimal modeling error in nonlinear dynamics [Conference Presenation]. https://doi.org/10.2172/1854319 Publication ID: 77460
  • Desai, A., Sudhi, P.V., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Domain Decomposition of Stochastic PDEs using FEniCS [Conference Presenation]. https://doi.org/10.2172/1859682 Publication ID: 77757
  • Sargsyan, K., Safta, C., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Boll, L., Zeng, X., Debusschere, B.J., & Debusschere, B.J. (2021). UQTk Version 3.1.1 User Manual. https://doi.org/10.2172/1777090 Publication ID: 103436
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Nonlinear sparse Bayesian learning for physics-based models. Journal of Computational Physics, 426. https://doi.org/10.1016/j.jcp.2020.109728 Publication ID: 66655
  • Sharma, S., Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2021). Scalable domain decomposition algorithms for uncertainty quantification: three-dimensional and time-dependent SPDEs [Conference Poster]. https://doi.org/10.2172/1847594 Publication ID: 77256
  • Coe, R.G., Bacelli, G., Gaebele, D.T., Cotten, A., McNatt, C., Wilson, D.G., Weaver, W., Kasper, J.L., Khalil, M., Dallman, A., & Dallman, A. (2021). Modeling and predicting power from a WEC array [Conference Presenation]. Oceans Conference Record (IEEE). https://doi.org/10.2172/1887348 Publication ID: 75640
  • Desai, A., Sudhi, P.V., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2020). Domain Decomposition of Time-Dependent Stochastic PDEs [Conference Presenation]. https://doi.org/10.2172/1835957 Publication ID: 72186
  • Robinson, B., da Costa, L., Poirel, D., Pettit, C., Khalil, M., Sarkar, A., & Sarkar, A. (2020). Aeroelastic oscillations of a pitching flexible wing with structural geometric nonlinearities: Theory and numerical simulation. Journal of Sound and Vibration, 484. https://doi.org/10.1016/j.jsv.2020.115389 Publication ID: 66907
  • Khalil, M., Teichert, G.H., Alleman, C., Heckman, N.M., Jones, R.E., Garikipati, K., Boyce, B.L., & Boyce, B.L. (2020). Modeling strength and failure variability due to porosity in additively manufactured metals. Computer Methods in Applied Mechanics and Engineering, 373. https://doi.org/10.1016/j.cma.2020.113471 Publication ID: 66937
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2020). Inference Of Model Sparsity In Nonlinear Dynamics Using Noisy Data [Conference Poster]. https://www.osti.gov/biblio/1818438 Publication ID: 74719
  • Desai, A., Sudhi, P.V., Khalil, M., Pettit, C., Poirel, D., Sarkar, ., Abhijit, ., & Abhijit, . (2020). Domain Decomposition of Stochastic PDEs: Three-dimensional and Time-dependent Systems [Conference Poster]. https://www.osti.gov/biblio/1818439 Publication ID: 74720
  • Sharma, S., Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2020). Domain Decomposition of Stochastic PDEs: Three-dimensional and Time-dependent Systems [Conference Poster]. https://www.osti.gov/biblio/1811967 Publication ID: 74308
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2020). Inference of model sparsity in nonlinear dynamics using noisy data [Conference Poster]. https://www.osti.gov/biblio/1811968 Publication ID: 74309
  • Safta, C., Ray, J., Bachman, W.B., Catanach, T.A., Chowdhary, K., Debusschere, B.J., Galvan, E., Geraci, G., Khalil, M., Portone, T., & Portone, T. (2020). Characterization of Partially Observed Epidemics – Application to COVID-19. https://doi.org/10.2172/1763554 Publication ID: 103028
  • Adams, B.M., Bohnhoff, W.J., Dalbey, K., Ebeida, M., Eddy, J.P., Eldred, M., Hooper, R., Hough, P.D., Hu, K., Jakeman, J.D., Khalil, M., Maupin, K.A., Monschke, J.A., Ridgway, E.M., Rushdi, A., Seidl, D.T., Stephens, J.A., Swiler, L.P., Winokur, J., & Winokur, J. (2020). Dakota, A Multilevel Parallel Object-Oriented Framework for Design Optimization Parameter Estimation Uncertainty Quantification and Sensitivity Analysis: Version 6.12 User’s Manual. https://doi.org/10.2172/1630694 Publication ID: 106076
  • Sargsyan, K., Safta, C., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Zeng, X., Debusschere, B.J., & Debusschere, B.J. (2020). UQTk User Manual (V.3.1.0). https://doi.org/10.2172/1605051 Publication ID: 105620
  • 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
  • Khalil, M. (2020). An Overview of Sequential Data Assimilation for Nonlinear Dynamical Systems [Presentation]. https://www.osti.gov/biblio/1768352 Publication ID: 72820
  • Dallman, A., Khalil, M., Raghukumar, K., Jones, C., Kasper, J., Flanary, C., Chang, G., Roberts, J.D., & Roberts, J.D. (2020). Wave data assimilation in support of wave energy converter powerprediction: Yakutat, Alaska case study [Conference Poster]. Proceedings of the Annual Offshore Technology Conference. https://doi.org/10.4043/30613-MS Publication ID: 72803
  • Dallman, A., Khalil, M., Raghukumar, K., Jones, C., Kasper, J., Flanary, C., Chang, G., Roberts, J.D., & Roberts, J.D. (2020). Wave data assimilation in support of wave energy converter powerprediction: Yakutat, Alaska case study [Conference Poster]. Proceedings of the Annual Offshore Technology Conference. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85086266355&origin=inward Publication ID: 73398
  • Khalil, M. (2019). Sequential Data Assimilation for Nonlinear Dynamical Systems [Presentation]. https://www.osti.gov/biblio/1646231 Publication ID: 66191
  • Khalil, M. (2019). Sparse Bayesian learning of nonlinear physics-based models [Presentation]. https://www.osti.gov/biblio/1646113 Publication ID: 65872
  • Jones, R.E., Boyce, B.L., Frankel, A., Heckman, N.M., Khalil, M., Ostien, J.T., Rizzi, F., Tachida, K., Teichert, G.H., Templeton, J.A., & Templeton, J.A. (2019). Uncertainty Quantification of Microstructural Material Variability Effects. https://doi.org/10.2172/1814062 Publication ID: 65085
  • Robinson, B., Poirel, D., Pettit, C., Khalil, M., Sarkar, A., & Sarkar, A. (2019). Aeroelastic oscillations of a pitching cantilever with structural and aerodynamic nonlinearities [Conference Poster]. https://www.osti.gov/biblio/1641578 Publication ID: 70378
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2019). Inferring model sparsity in nonlinear fluid-structure interaction systems using noisy wind-tunnel data [Conference Poster]. https://www.osti.gov/biblio/1641580 Publication ID: 70380
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2019). Sparse learning of nonlinear physics-based models [Conference Poster]. https://www.osti.gov/biblio/1641097 Publication ID: 69633
  • Rizzi, F., Khalil, M., Jones, R.E., Templeton, J.A., Ostien, J.T., Boyce, B.L., & Boyce, B.L. (2019). Bayesian modeling of inconsistent plastic response due to material variability. Computer Methods in Applied Mechanics and Engineering, 353(C), pp. 183-200. https://doi.org/10.1016/j.cma.2019.05.012 Publication ID: 64718
  • Khalil, M., Safta, C., & Safta, C. (2019). A Parallel Transitional MCMC for Robust PDF Sampling – New UQTk Capability [Presentation]. https://www.osti.gov/biblio/1645251 Publication ID: 68541
  • Lu, H., Chen, J., Wu, X., Fu, X., Khalil, M., Safta, C., Huang, Y., & Huang, Y. (2019). Sparse PCE surrogate assisted inversion algorithm for ultra-deep electromagnetic resistivity logging-while-drilling data [Conference Poster]. https://www.osti.gov/biblio/1639627 Publication ID: 67856
  • Jones, R.E., Alleman, C., Frankel, A., Khalil, M., Heckman, N.M., Boyce, B.L., Teichert, G., & Teichert, G. (2019). Modeling material variability with uncertainty quantification and machine learning techniques [Presentation]. https://www.osti.gov/biblio/1648628 Publication ID: 67524
  • Carlberg, K.T., Guzzetti, S., Khalil, M., Sargsyan, K., & Sargsyan, K. (2019). Large-Scale Uncertainty Propagation via Overlapping Domain Decomposition [Conference Poster]. https://www.osti.gov/biblio/1602394 Publication ID: 67151
  • Khalil, M. (2019). Data assimilation for joint state and parameter estimation: nonlinear filtering [Presentation]. https://www.osti.gov/biblio/1598433 Publication ID: 65152
  • Dallman, A., Khalil, M., Jones, C., Kasper, J., Flanary, C., Roberts, J.D., & Roberts, J.D. (2018). A Case Study of Wave Energy Forecast Improvement Using Data Assimilation [Conference Poster]. https://www.osti.gov/biblio/1761389 Publication ID: 60650
  • Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Teichert, G., Garikipati, K., & Garikipati, K. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592993 Publication ID: 59782
  • Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Garikipati, K., Teichert, G., & Teichert, G. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592995 Publication ID: 59784
  • Jones, R.E., Rizzi, F., Templeton, J.A., Ostien, J.T., Alleman, C., Khalil, M., Frankel, A., Heckman, N.M., Boyce, B.L., Garikipati, K., Teichert, G., & Teichert, G. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Conference Poster]. https://www.osti.gov/biblio/1592996 Publication ID: 59785
  • Dallman, A., Khalil, M., Raghukumar, K., Kasper, J., Jones, C., Roberts, J.D., & Roberts, J.D. (2018). Improved Wave Energy Production Forecasts for Smart Grid Integration. https://doi.org/10.2172/1531318 Publication ID: 59305
  • 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
  • Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2018). Domain Decomposition of Stochastic PDEs – New Developments [Conference Poster]. https://www.osti.gov/biblio/1806663 Publication ID: 63314
  • Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2018). Scalable domain decomposition solvers for stochastic PDEs in high performance computing. Computer Methods in Applied Mechanics and Engineering, 335, pp. 194-222. https://doi.org/10.1016/j.cma.2017.09.006 Publication ID: 47213
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2018). Data-driven model reduction using sparse Bayesian learning: Application to nonlinear aeroelastic system [Conference Poster]. https://www.osti.gov/biblio/1525934 Publication ID: 62475
  • Sandhu, R., Pettit, C., Khalil, M., Sarkar, A., Poirel, D., & Poirel, D. (2018). Automatic relevance determination priors in Bayesian model selection: Application to nonlinear fluid-structure interaction systems [Conference Poster]. https://www.osti.gov/biblio/1524946 Publication ID: 62362
  • Bisaillon, P., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2018). Bayesian Selection of Optimal Physics-Based Model and Modeling Error for Nonlinear Dynamical Systems [Conference Poster]. https://www.osti.gov/biblio/1524947 Publication ID: 62363
  • Robinson, B., Sandhu, R., Khalil, M., Poirel, D., Pettit, C., Sarkar, A., & Sarkar, A. (2018). Global sensitivity analysis for nonlinear aeroelastic vibrations of a cantilever [Conference Poster]. https://www.osti.gov/biblio/1524948 Publication ID: 62364
  • Khalil, M., Rizzi, F., Frankel, A., Alleman, C., Templeton, J.A., Ostien, J.T., Boyce, B.L., Jones, R.E., & Jones, R.E. (2018). Embedded Model Error and Bayesian Model Selection for Material Variability [Conference Poster]. https://www.osti.gov/biblio/1508918 Publication ID: 61655
  • Sandhu, S., Pettit, C., Khalil, M., Sarkar, A., Poirel, D., & Poirel, D. (2018). Bayesian Model Reduction using Automatic Relevance Determination (ARD): Observations and Improvements [Conference Poster]. https://www.osti.gov/biblio/1510685 Publication ID: 61752
  • Alleman, C., Boyce, B.L., Frankel, A., Heckman, N.M., Khalil, M., Garikipati, K., Jones, R.E., & Jones, R.E. (2018). Modeling material variability with uncertainty quantification and machine learning techniques [Presentation]. https://www.osti.gov/biblio/1504569 Publication ID: 61315
  • Khalil, M. (2018). Data-Driven Bayesian Model Selection: Parameter Space Dimension Reduction using Automatic Relevance Determination Priors [Presentation]. https://www.osti.gov/biblio/1806477 Publication ID: 58735
  • Khalil, M., Brubaker, E., Hilton, N.R., Kupinski, M.A., MacGahan, C.J., Marleau, P.A., & Marleau, P.A. (2017). Null-hypothesis testing using distance metrics for verification of arms-control treaties [Conference Poster]. 2016 IEEE Nuclear Science Symposium, Medical Imaging Conference and Room-Temperature Semiconductor Detector Workshop, NSS/MIC/RTSD 2016. https://doi.org/10.1109/NSSMIC.2016.8069935 Publication ID: 47572
  • Robinson, B., Rocha da Costa, L.J., Poirel, D., Pettit, C., Khalil, M., Sarkar, A., & Sarkar, A. (2017). Aeroelastic oscillations of a cantilever with structural nonlinearities: theory and numerical simulation. Journal of Sound and Vibration. https://www.osti.gov/biblio/1429633 Publication ID: 97968
  • 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
  • Sandhu, R., Rocha da Costa, L.J., Robinson, B., Matachniouk, A., Chajjed, S., Bisaillon, P., Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2017). An integrated approach for fluid-structure interaction: uncertainty quantifi%0Ccation Bayesian inference scalable algorithms for high performance computing and wind tunnel testing [Conference Poster]. https://www.osti.gov/biblio/1507263 Publication ID: 57363
  • Desai, A., Bisaillon, P., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2017). A Scalable Sampling-Free Nonlinear State Estimation Algorithm for Large-Scale Models and Data Sets using High Performance Computing [Conference Poster]. https://www.osti.gov/biblio/1507264 Publication ID: 57364
  • Robinson, B., Rocha da Costa, L.J., Poirel, D., Pettit, C., Khalil, M., Sarkar, A., & Sarkar, A. (2017). Large amplitude aeroelastic oscillations of a cantilever with structural and aerodynamic nonlinearities: Theory and wind tunnel test [Conference Poster]. https://www.osti.gov/biblio/1507267 Publication ID: 57368
  • Khalil, M., Lee, J., Salloum, M., & Salloum, M. (2017). Predictive Modeling of Wavelet Coefficients for Physical Processes [Conference Poster]. https://www.osti.gov/biblio/1507268 Publication ID: 57369
  • Sandhu, R., Pettit, C., Khalil, M., Sarkar, A., Poirel, D., & Poirel, D. (2017). Bayesian Model Selection in Continuous Model Domain Using Automatic Relevance Determination with Applications to Nonlinear Aeroelasticity [Conference Poster]. https://www.osti.gov/biblio/1507087 Publication ID: 57370
  • Bisaillon, P., Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2017). Colored Process Noise in Nonlinear Aeroelastic Systems [Conference Poster]. https://www.osti.gov/biblio/1507086 Publication ID: 57371
  • 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
  • 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
  • Khalil, M. (2017). Data assimilation: nonlinear filtering [Presentation]. https://www.osti.gov/biblio/1456722 Publication ID: 56093
  • 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
  • Khalil, M. (2017). Data-Driven Bayesian Model Selection: Parameter Space Dimension Reduction using Automatic Relevance Determination Priors [Conference Poster]. https://www.osti.gov/biblio/1456336 Publication ID: 55482
  • 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., 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
  • Khalil, M., Brubaker, E., Hilton, N.R., Kupinski, M.A., MacGahan, C.J., Marleau, P.A., & Marleau, P.A. (2016). Null-Hypothesis Testing Using Distance Metrics for Verification of Arms-Control Treaties [Conference Poster]. https://doi.org/10.1109/NSSMIC.2016.8069935 Publication ID: 48081
  • Bauer, J., Ray, J., Khalil, M., & Khalil, M. (2016). SUMMIT Wildfire App: A SUMMIT application leveraging new R&D capabilities [Presentation]. https://www.osti.gov/biblio/1428157 Publication ID: 47987
  • 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
  • Carlberg, K.T., Guzzetta, S.L., Khalil, M., Sargsyan, K., & Sargsyan, K. (2016). Uncertainty Propagation in (large-scale) Networks via Domain Decomposition [Presentation]. https://www.osti.gov/biblio/1393766 Publication ID: 52227
  • Ray, J., Lefantzi, S., Bauer, J., Khalil, M., Rothfuss, A.J., Cauthen, K.R., Finley, P.D., Smith, H., & Smith, H. (2016). Online mapping and forecasting of epidemics using open-source indicators. https://doi.org/10.2172/1562406 Publication ID: 52362
  • 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
  • Khalil, M., Poirel, D., Sarkar, A., & Sarkar, A. (2016). Bayesian analysis of the flutter margin method in aeroelasticity. Journal of Sound and Vibration. https://doi.org/10.1016/j.jsv.2016.07.016 Publication ID: 50089
  • 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
  • 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
  • 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
  • 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
  • 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
  • Desai, A., Khalil, M., Sarkar, A., Pettit, C., Poirel, D., & Poirel, D. (2016). Domain Decomposition of Stochastic PDEs: High Resolution Computational Mesh with Large Stochastic Dimension [Conference Poster]. https://www.osti.gov/biblio/1369003 Publication ID: 50443
  • Sandhu, R., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2016). Efficient Computation of Evidence in Bayesian Inference using High Performance Computing [Conference Poster]. https://www.osti.gov/biblio/1369004 Publication ID: 50444
  • Bisaillon, P., Desai, A., Khalil, M., Pettit, C., Poirel, D., Sarkar, A., & Sarkar, A. (2016). A Scalable Sampling-Free Non-Gaussian Data Assimilation Algorithm for Large Scale Computational Models using Large Data Sets [Conference Poster]. https://www.osti.gov/biblio/1369005 Publication ID: 50445
  • 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
  • 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
  • Khalil, M. (2015). Hybrid approach to surrogate modeling [Presentation]. https://www.osti.gov/biblio/1324416 Publication ID: 45416
  • 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
  • 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
  • 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
  • Shao, J., Khalil, M., Najm, H.N., & Najm, H.N. (2014). uncertainty quantification [Presentation]. https://www.osti.gov/biblio/1496102 Publication ID: 37713
  • Khalil, M. (2014). CRF Webpage [Presentation]. https://www.osti.gov/biblio/1695598 Publication ID: 40407
Showing 10 of 112 publications.