Publications

Bishop, S.R., Armijo, K.M., Debusschere, B., McDaniel, A.H., Sugar, J.D., Witman, M.D., Ginley, D., Lany, S., Ma, Z., Zakutayev, A., Ogitsu, T., Wood, B., & Ding, D. (2024). HydroGEN: Solar Thermochemical Hydrogen Production [Conference Poster]. 10.2172/2564001

Bishop, S.R., Armijo, K.M., Debusschere, B., McDaniel, A.H., Sugar, J.D., Witman, M.D., Ginley, D., Lany, S., Ma, Z., Zakutayev, A., Ogitsu, T., Wood, B., & Ding, D. (2024). HydroGEN: Solar Thermochemical Hydrogen (STCH) Water Splitting [Poster]. 10.2172/2563874

Sargsyan, K., Debusschere, B., & Eldred, M. (2024). Surrogate-Accelerated Parameter Optimization for the Quasi-Biennial Oscillation [Conference Presentation]. 10.2172/2540415

Curry, C.J., Sargsyan, K., Safta, C., & Debusschere, B. (2024). The UQTk C++/Python Toolkit for Uncertainty Quantification: Overview and Applications [Conference Presentation]. 10.2172/2540447

Sargsyan, K., Debusschere, B., & Eldred, M. (2024). Surrogate-based calibration for the quasi-biennial oscillation [Conference Poster]. 10.2172/2540427

Debusschere, B., Curry, C.J., Seidl, D.T., Chang, K.W., & Mariner, P. (2023). Machine Learning Surrogates for Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presentation]. 10.2172/2430768

Debusschere, B., Curry, C.J., Harvey, J.A., Seidl, D.T., Chang, K.W., & Mariner, P. (2023). Machine Learning Surrogates for Time Dependent Fuel Degradation Processes in Nuclear Waste Repository Simulations [Conference Presentation]. 10.2172/2431917

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., & Mariner, P. (2023). Machine Learning Surrogates of a Fuel Matrix Degradation Process Model for Performance Assessment of a Nuclear Waste Repository. Nuclear Technology, 209(9), pp. 1295-1318. 10.1080/00295450.2023.2197666

Debusschere, B., & Benedict, J. (2022). Surrogate Models for Improving Atmospheric Modeling [Conference Presentation]. 10.2172/2432246

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Presentation]. 10.2172/2006027

Mariner, P., Debusschere, B., Fukuyama, D.E., Harvey, J.A., Laforce, T.C., Leone, R.C., Foulk, J.W., Swiler, L.P., & Taconi, A.M. (2022). GDSA Framework Development and Process Model Integration FY2022. 10.2172/1893995

Bennett, C., Xiao, T.P., Liu, S., Humphrey, L., Incorvia, J.A., Debusschere, B., Ries, D., & Agarwal, S. (2022). Probabilistic Nanomagnetic Memories for Uncertain and Robust Machine Learning. 10.2172/1891190

Thorpe, J.E., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., & Debusschere, B. (2022). Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Presentation]. ACM International Conference Proceeding Series. 10.2172/2004219

Thorpe, J.E., Swiler, L.P., Tarman, T.D., Hanson, S.T., Cruz, G.J., Debusschere, B., & Rollins, T. (2022). Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Paper]. https://www.osti.gov/biblio/2003842

Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Arguello, B., Geraci, G., Debusschere, B., Hanson, S.T., Outkin, A.V., Thorpe, J.E., Hart, W.E., Sahakian, M.A., Gabert, K.G., Glatter, C., Johnson, E.S., & Punla-Green, S. (2022). Uncertainty Quantification within SECURE LDRD [Presentation]. https://www.osti.gov/biblio/2003628

Boll, L., Johnston, K., Sargsyan, K., Safta, C., & Debusschere, B. (2022). The UQTk C++/Python Toolkit for Uncertainty Quantification: Overview and Applications [Conference Presentation]. 10.2172/2002312

Swiler, L.P., Portone, T., Mariner, P., Leone, R., Brooks, D.M., Seidl, D.T., Debusschere, B., & Berg, T. (2022). Use of a machine learning model for a constitutive chemistry model within a groundwater flow and transport application modeling nuclear fuel degradation in a waste repository [Conference Presentation]. 10.2172/2002228

Thorpe, J.E., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., & Debusschere, B. (2022). WiP: Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Paper]. https://www.osti.gov/biblio/2002055

Thorpe, J.E., Swiler, L.P., Hanson, S.T., Cruz, G.J., Tarman, T.D., Rollins, T., & Debusschere, B. (2022). WiP: Verification of Cyber Emulation Experiments Through Virtual Machine and Host Metrics [Conference Presentation]. 10.2172/2002078

Liu, S., Xiao, T.P., Agarwal, S., Debusschere, B., Bennett, C., & Incorvia, J.A. (2022). Domain wall-magnetic tunnel junction synapses for Bayesian neural networks [Presentation]. https://www.osti.gov/biblio/2001939

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High-Level Radioactive Waste Management Conference, IHLRWM 2022, Embedded with the 2022 ANS Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward

Debusschere, B., Seidl, D.T., Berg, T.M., Chang, K.W., Leone, R.C., Swiler, L.P., & Mariner, P. (2022). Machine Learning Surrogate Process Models for Efficient Performance Assessment of a Nuclear Waste Repository [Conference Paper]. Proceedings of the International High Level Radioactive Waste Management Conference Ihlrwm 2022 Embedded with the 2022 Ans Winter Meeting. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85181567561&origin=inward

Sargsyan, K., Safta, C., Boll, L., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Zeng, X., & Debusschere, B. (2021). UQTk Version 3.1.2 User Manual. 10.2172/1855040

Mariner, P., Berg, T.M., Debusschere, B., Eckert, A., Harvey, J.A., Laforce, T.C., Leone, R.C., Mills, M.M., Nole, M.A., Park, H.D., Perry, F.V., Seidl, D.T., Swiler, L.P., & Chang, K.W. (2021). GDSA Framework Development and Process Model Integration FY2021. 10.2172/1825056

Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Cruz, G.J., Arguello, B., Geraci, G., Debusschere, B., Hanson, S.T., Outkin, A.V., Thorpe, J.E., Hart, W.E., Sahakian, M.A., Gabert, K.G., Glatter, C., Johnson, E.S., & Punla-Green, S. (2021). Science and Engineering of Cybersecurity by Uncertainty quantification and Rigorous Experimentation (SECURE) (Final Report). 10.2172/1821322

Pinar, A., Tarman, T.D., Swiler, L.P., Gearhart, J.L., Hart, D., Vugrin, E., Cruz, G.J., Arguello, B., Geraci, G., Debusschere, B., Hanson, S.T., Outkin, A.V., Thorpe, J.E., Hart, W.E., Sahakian, M.A., Gabert, K.G., Glatter, C., Johnson, E.S., & Punla-Green, A.S. (2021). Science & Engineering of Cyber Security by Uncertainty Quantification and Rigorous Experimentation (SECURE) HANDBOOK. 10.2172/1820527

Geraci, G., Swiler, L.P., & Debusschere, B. (2021). Multifidelity UQ sampling for Stochastic Simulations [Conference Presentation]. 10.2172/1889573

Boll, L., Johnston, K., McDaniel, A.H., Stechel, E., & Debusschere, B. (2021). Inference of Hydrogen RedOx Reactions Models using Bayesian Compressive Sensing [Conference Presentation]. 10.2172/1891955

Berg, T.M., Mariner, P., Debusschere, B., Seidl, D.T., Leone, R.C., & Chang, K.W. (2021). Machine Learning Surrogates for the Fuel Matrix Degradation Model [Presentation]. https://www.osti.gov/biblio/1869760

Sargsyan, K., Safta, C., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Boll, L., Zeng, X., & Debusschere, B. (2021). UQTk Version 3.1.1 User Manual. 10.2172/1777090

Berg, T.M., Chang, K.W., Leone, R.C., Seidl, D.T., Mariner, P., & Debusschere, B. (2021). Surrogate Modeling of Spent Fuel Degradation for Repository Performance Assessment [Conference Presentation]. 10.2172/1854307

Johnston, K., Debusschere, B., & Stechel, E. (2021). Bayesian Model Selection for Thermodynamic Models of Redox Active Metal Oxides [Conference Presentation]. 10.2172/1853871

Debusschere, B., Geraci, G., Jakeman, J.D., Safta, C., & Swiler, L.P. (2021). Polynomial Chaos Expansions for Discrete Random Variables in Cyber Security Emulytics Experiments [Conference Presentation]. 10.2172/1847628

Geraci, G., Crussell, J., Swiler, L.P., & Debusschere, B. (2021). Exploration of multifidelity UQ sampling strategies for computer network applications. International Journal for Uncertainty Quantification, 11(1), pp. 55-91. 10.1615/Int.J.UncertaintyQuantification.2021033774

Mariner, P., Nole, M.A., Basurto, E., Berg, T.M., Chang, K.W., Debusschere, B., Eckert, A., Ebeida, M., Gross, M., Hammond, G., Harvey, J.A., Jordan, S.H., Kuhlman, K.L., Laforce, T.C., Leone, R.C., McLendon, W., Mills, M.M., Park, H.D., Foulk, J.W., … Swiler, L.P. (2020). Advances in GDSA Framework Development and Process Model Integration. 10.2172/1671380

Mariner, P., Berg, T.M., Chang, K.W., Debusschere, B., Leone, R.C., & Seidl, D.T. (2020). Surrogate Model Development of Spent Fuel Degradation for Repository Performance Assessment. 10.2172/1673178

Johnston, K., Boll, L., Sargsyan, K., Safta, C., & Debusschere, B. (2020). UQTk A C++/Python Toolkit for Uncertainty Quantification: Overview and Applications [Presentation]. https://www.osti.gov/biblio/1820270

Safta, C., Ray, J., Foulk, J.W., Catanach, T.A., Chowdhary, K., Debusschere, B., Galvan, E., Geraci, G., Khalil, M., & Portone, T. (2020). Characterization of Partially Observed Epidemics - Application to COVID-19. 10.2172/1763554

Sargsyan, K., Safta, C., Johnston, K., Khalil, M., Chowdhary, K., Rai, P., Casey, T., Zeng, X., & Debusschere, B. (2020). UQTk User Manual (V.3.1.0). 10.2172/1605051

Mariner, P., Debusschere, B., Hammond, G.E., Seidl, D.T., Laura, S., & Vo, J. (2019). Surrogate Modeling of Spatially Heterogeneous Source Terms for Probabilistic Assessment of Repository Performance [Conference Poster]. https://www.osti.gov/biblio/1763614

Mariner, P., Debusschere, B., Jerden, J., Seidl, D.T., Swiler, L.P., & Vo, J. (2019). Lessons Learned in the Development of Source Term Surrogate Models for Repository Performance Assessment [Conference Poster]. https://www.osti.gov/biblio/1643084

Swiler, L.P., Geraci, G., Debusschere, B., & Crussell, J. (2019). Uncertainty Quantification in Cyber Emulation [Conference Poster]. https://www.osti.gov/biblio/1642954

Mariner, P., Connolly, L.A., Cunningham, L., Debusschere, B., Dobson, D.C., Frederick, J.M., Hammond, G.E., Jordan, S.H., Laforce, T.C., Nole, M.A., Park, H.D., Foulk, J.W., Rogers, R., Seidl, D.T., Sevougian, S.D., Stein, E., Swift, P., Swiler, L.P., Vo, J., & Wallace, M. (2019). Progress in Deep Geologic Disposal Safety Assessment in the U.S. since 2010. 10.2172/1570094

Casey, T., & Debusschere, B. (2019). Analysis of Neural Network Combustion Surrogate Models. 10.2172/1569154

Casey, T., Debusschere, B., Eldred, M., Geraci, G., Ghanem, R., Jakeman, J.D., Marzouk, Y., Najm, H.N., Safta, C., & Sargsyan, K. (2019). FASTMath: UQ Algorithms [Conference Poster]. https://www.osti.gov/biblio/1641088

Carpenter, J.H., Robinson, A.C., & Debusschere, B. (2019). Verification of Uncertainty Quantification\\for Equation of State Table [Conference Poster]. https://www.osti.gov/biblio/1640804

Mariner, P., Seidl, D.T., Swiler, L.P., Debusschere, B., Vo, J., Jerden, J., & Frederick, J.M. (2019). Surrogate Modeling of Fuel Dissolution [Presentation]. https://www.osti.gov/biblio/1648825

Geraci, G., Swiler, L.P., Crussell, J., & Debusschere, B. (2019). EXPLORATION OF MULTIFIDELITY APPROACHES FOR UNCERTAINTY QUANTIFICATION IN NETWORK APPLICATIONS [Conference Poster]. 10.7712/120219.6352.18797

Geraci, G., Swiler, L.P., Crussell, J., & Debusschere, B. (2019). Exploration of multifidelity UQ strategies for network applications [Presentation]. https://www.osti.gov/biblio/1644472

Swiler, L.P., Debusschere, B., Geraci, G., & Crussell, J. (2019). SECURE Uncertainty Quantification Thrust [Presentation]. https://www.osti.gov/biblio/1644470

Mariner, P., Swiler, L.P., Seidl, D.T., Debusschere, B., Vo, J., & Frederick, J.M. (2019). High Fidelity Surrogate Modeling of Fuel Dissolution for Probabilistic Assessment of Repository Performance [Conference Poster]. https://www.osti.gov/biblio/1639277

Debusschere, B., Sargsyan, K., & Safta, C. (2019). UQTk A Flexible Python/C++ Toolkit for Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1602639

Geraci, G., Swiler, L.P., Crussell, J., & Debusschere, B. (2019). Exploration of multifidelity approaches for uncertainty quantification in network applications [Conference Poster]. Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019. 10.7712/120219.6352.18797

Mariner, P., Swiler, L.P., Seidl, D.T., Debusschere, B., Vo, J., & Frederick, J.M. (2019). High fidelity surrogate modeling of fuel dissolution for probabilistic assessment of repository performance [Conference Poster]. International High-Level Radioactive Waste Management 2019, IHLRWM 2019. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85067129626&origin=inward

Debusschere, B. (2018). Acknowledging Variability in Scientific Computing [Conference Poster]. https://www.osti.gov/biblio/1576179

Rohlig, K., Plischke, E., Becker, D., Stein, E., Govaerts, J., Debusschere, B., Koskinen, L., Kupiainen, P., Leigh, C., Mariner, P., Nummi, O., Pastina, B., Sevougian, S.D., Spiessl, S., Swiler, L.P., Weetjens, E., & Zeitler, T. (2018). Sensitivity Analysis in Performance Assessment: Towards a Joint Approach [Conference Poster]. https://www.osti.gov/biblio/1806880

Rohlig, K., Plischke, E., Becker, D., Stein, E., Govaerts, J., Debusschere, B., Koskinen, L., Kupiainen, P., Leigh, C., Mariner, P., Nummi, O., Pastina, B., Sevougian, S.D., Spiessl, S., Swiler, L.P., Weetjens, E., & Zeitler, T. (2018). Sensitivity Analysis in Performance Assessment: Towards a Joint Approach [Conference Poster]. https://www.osti.gov/biblio/1568977

Debusschere, B., Sargsyan, K., & Parekh, O.D. (2018). Quantum Annealing Approaches for Building Sparse Surrogate Models in Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1806634

Debusschere, B., Sargsyan, K., Safta, C., Rai, P., & Chowdhary, K. (2018). UQTk: A Flexible Python/C++ Toolkit for Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1510846

Najm, H.N., Debusschere, B., 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. (2018). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ? [Conference Poster]. https://www.osti.gov/biblio/1572447

Debusschere, B., Safta, C., Sargsyan, K., & Chowdhary, K. (2018). UQTk: A C++/Python Uncertainty Quantification Toolkit [Presentation]. https://www.osti.gov/biblio/1497545

Debusschere, B., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., & Najm, H.N. (2018). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1512381

Debusschere, B., Templeton, J.A., Safta, C., Sargsyan, K., Pinar, A., & Najm, H.N. (2017). Predictive Fidelity of Machine Learning Methods Applied to Scientific Simulations [Conference Poster]. https://www.osti.gov/biblio/1513665

Sargsyan, K., Safta, C., Chowdhary, K., Castorena, S., de Bord, S., & Debusschere, B. (2017). UQTk Version 3.0.4 User Manual. 10.2172/1813904

Debusschere, B., Rizzi, F., Foulk, J.W., Sargsyan, K., Safta, C., Najm, H.N., Knio, O., Mycek, P., Contreras, A., & le Olivier, M. (2017). Probabilistic Approach to Enable Extreme-Scale Simulations under Uncertainty and System Faults [Conference Poster]. https://www.osti.gov/biblio/1470926

Najm, H.N., Debusschere, B., 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. (2017). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine [Conference Poster]. https://www.osti.gov/biblio/1574094

Debusschere, B., Sadler, L.E., Antoun, B.R., Templeton, J.A., Kolda, T.G., & May, E. (2017). Improved Equity Diversity and Inclusion to Sustain an Effective Applied Mathematics Workforce [Conference Poster]. https://www.osti.gov/biblio/1466495

Debusschere, B., Pinar, A., Sargsyan, K., Templeton, J.A., & 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

Debusschere, B., Safta, C., Sargsyan, K., & Chowdhary, K. (2017). Polynomial Chaos Based Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1463445

Debusschere, B., Safta, C., Sargsyan, K., & Chowdhary, K. (2017). Polynomial Chaos Based Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1513464

Debusschere, B. (2017). Intrusive polynomial chaos methods for forward uncertainty propagation. Handbook of Uncertainty Quantification. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85034776867&origin=inward

Rizzi, F., Foulk, J.W., Cook, B., Dahlgren, K., Sargsyan, K., Mycek, P., Lemaitre, O., Knio, O., & Debusschere, B. (2017). Scaling and Energy Analysis for a Resilient ULFM-Based PDE Solver [Conference Poster]. https://www.osti.gov/biblio/1476821

Foulk, J.W., Rizzi, F., Cook, B., Dahlgren, K., Sargsyan, K., Mycek, P., le Maitre, O., Knio, O., & Debusschere, B. (2017). A Resilient ULFM-based PDE Solver: Performance Scaling and Energy Analysis [Conference Poster]. 10.1109/ScalA.2016.010

Foulk, J.W., Rizzi, F., Sargsyan, K., Dahlgren, K., Debusschere, B., Cook, B., Mycek, P., le Maitre, O., & Knio, O. (2017). Performance Scaling Variability and Energy Analysis for a Resilient ULFM-based PDE Solver [Presentation]. 10.1109/ScalA.2016.010

Debusschere, B. (2017). Building a Partial Differential Equations Solver that is Resilient to Soft and Hard Faults [Presentation]. https://www.osti.gov/biblio/1458031

Debusschere, B. (2017). Bayesian Calibration: An introductory tutorial [Presentation]. https://www.osti.gov/biblio/1524281

Carpenter, J.H., Robinson, A.C., & Debusschere, B. (2017). Incorporating Data Uncertainty into Equation of State Tables [Conference Poster]. https://www.osti.gov/biblio/1458122

Sargsyan, K., Safta, C., Chowdhary, K., Castorena, S., de Bord, S., & Debusschere, B. (2017). UQTk Version 3.0.3 User Manual. 10.2172/1367452

Rizzi, F., Foulk, J.W., Sargsyan, K., Mycek, P., Contreras, A., Safta, C., le Maitre, O., Knio, O., & Debusschere, B. (2017). Partial Differential Equations Solver Resilient to Soft and Hard Faults [Conference Poster]. https://www.osti.gov/biblio/1424869

Debusschere, B. (2017). Uncertainty Quantification Toolkit [Presentation]. https://www.osti.gov/biblio/1458261

Sargsyan, K., Safta, C., Najm, H.N., Debusschere, B., Ricciuto, D., & Thornton, P. (2016). Weighted Iterative Bayesian Compressive Sensing (WIBCS) for High Dimensional Polynomial Surrogate Construction [Conference Poster]. https://www.osti.gov/biblio/1576186

Najm, H.N., Debusschere, B., 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. (2016). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine ? ScramjetUQ ? [Conference Poster]. https://www.osti.gov/biblio/1420843

Rizzi, F., Foulk, J.W., Cook, B., Sargsyan, K., Mycek, P., le Maitre, O., Knio, O., Dahlgren, K., & Debusschere, B. (2016). Performance Scaling Variability and Energy Analysis for a Resilient ULFM-based PDE Solver [Conference Poster]. https://www.osti.gov/biblio/1408949

Najm, H.N., Debusschere, B., 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. (2016). Uncertainty Quantification in LES Computations of Turbulent Multiphase Combustion in a Scramjet Engine [Conference Poster]. https://www.osti.gov/biblio/1397105

Sargsyan, K., Safta, C., Chowdhary, K., Castorena, S., de Bord, S., & Debusschere, B. (2016). UQTk (V. 3.0) User Manual. 10.2172/1562399

Carpenter, J.H., Debusschere, B., & Robinson, A.C. (2016). Integrated Material Models for Equation of State and Transport Properties for Aluminum Supporting Uncertainty Quantification [Presentation]. https://www.osti.gov/biblio/1527265

Castorena, S.M., & Debusschere, B. (2016). Why Are Dual Pane Windows Better? [Presentation]. https://www.osti.gov/biblio/1373065

Dahlgren, K.M., Rizzi, F., Foulk, J.W., & Debusschere, B. (2016). Rexsss Parallel Distributed PDE Solver Sensitivity Analysis [Presentation]. https://www.osti.gov/biblio/1373063

Dahlgren, K.M., Rizzi, F., Foulk, J.W., & Debusschere, B. (2016). Rexsss Parallel Distributed PDE Solver Sensitivity Analysis [Presentation]. https://www.osti.gov/biblio/1373062

Carpenter, J.H., Robinson, A.C., Debusschere, B., & Wills, A.E. (2016). Accounting for Data Uncertainty in Making and Using an EOS [Presentation]. https://www.osti.gov/biblio/1369601

Rizzi, F., Foulk, J.W., Sargsyan, K., Mycek, P., Safta, C., le Maitre, O., Knio, O., & Debusschere, B. (2016). ULFM-MPI Implementation of a Resilient Task-Based Partial Differential Equations Preconditioner [Conference Poster]. https://doi.org/10.1145/2909428.2909429

Rizzi, F., Foulk, J.W., Sargsyan, K., Mycek, P., Safta, C., le Maitre, O., Knio, O., & Debusschere, B. (2016). ULFM-MPI Implementation of a Resilient Task-Based Partial Differential Equations Preconditioner [Poster]. 10.2172/1561476

Foulk, J.W., Rizzi, F., Sargsyan, K., Dahlgren, K., Mycek, P., Safta, C., le Maitre, O., Knio, O., & Debusschere, B. (2016). Scalability of Partial Differential Equations Preconditioner Resilient to Soft and Hard Faults [Conference Poster]. https://doi.org/10.1007/978-3-319-41321-1_24

Foulk, J.W., Rizzi, F., Sargsyan, K., Dahlgren, K., Mycek, P., Safta, C., le Maitre, O., Knio, O., & Debusschere, B. (2016). Scalability of Partial Differential Equations Preconditioner Resilient to Soft and Hard Faults [Poster]. 10.2172/1561477

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