Publications

Seelinger, L., Reinarz, A., Lykkegaard, M.B., Alghamdi, A.M.A., Aristoff, D., Bangerth, W., Benezech, J., Diez, M., Frey, K., Jakeman, J.D., Jorgensen, J.S., Kim, K., Martinelli, M., Parno, M., Pellegrini, R., Petra, N., Riis, N.A.B., Rosenfeld, K., Serani, A., … Scheichl, R. (2024). Democratizing uncertainty quantification [Presentation]. Journal of Computational Physics. 10.1016/j.jcp.2024.113542

Kadeethum, T., Jha, B., Verzi, S.J., Jakeman, J.D., Yoon, H., & Yoon, H. (2024). Probabilistic Interpretation of Improved Neural Operators for Large-Scale Geological Carbon Storage [Presentation]. 10.2172/2585754

Sun, X., Jakeman, A.J., Croke, B.F.W., Roberts, S.G., Jakeman, J.D., & Jakeman, J.D. (2024). Assessing convergence in global sensitivity analysis: a review of methods for assessing and monitoring convergence. Socio-Environmental Systems Modelling, 6. 10.18174/sesmo.18678

Kadeethum, T., Chang, K.W., Jakeman, J.D., Yoon, H., & Yoon, H. (2024). Enhancing predictive modeling in subsurface physics: an introduction to the progressive improved neural operator (p-INO) framework [Presentation]. 10.2172/2586056

Safta, C., Jakeman, J.D., Brashar, C.L., Boler, M.E., Gorodetsky, A.A., & Gorodetsky, A.A. (2024). Bayesian Framework for Forecasting Dynamical Systems in the Presence of Corrupted Data [Conference Presentation]. 10.2172/2430539

Safta, C., Jakeman, J.D., Brashar, C.L., Boler, M.E., Ortiz, J.B., & Ortiz, J.B. (2024). A Bayesian Framework for UQ in Dynamical Systems in the Presence of Corrupted Measurements [Conference Presentation]. 10.2172/2540428

Geraci, G., Zeng, X., Gorodetsky, A.A., Jakeman, J.D., Eldred, M., Ghanem, R., & Ghanem, R. (2024). Multi-Fidelity Sampling Estimators for Models with Dissimilar Parametrization [Conference Presentation]. 10.2172/2540454

Jakeman, J.D. (2024). PyApprox: a software package for sensitivity Analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling [Conference Presentation]. 10.2172/2540434

Kadeethum, T., Chang, K.W., Jakeman, J.D., Yoon, H., & Yoon, H. (2024). Progressive reduced order modeling: from single-phase flow to coupled multiphysics processes [Conference Proceeding]. 58th US Rock Mechanics / Geomechanics Symposium 2024, ARMA 2024. 10.2172/2586284

Jakeman, J.D. (2023). PyApprox: A software package for sensitivity analysis, Bayesian inference, optimal experimental design, and multi-fidelity uncertainty quantification and surrogate modeling. Environmental Modelling and Software, 170(1). 10.1016/j.envsoft.2023.105825

Zeng, X., Geraci, G., Eldred, M., Jakeman, J.D., Gorodetsky, A.A., Ghanem, R., & Ghanem, R. (2023). Multifidelity uncertainty quantification with models based on dissimilar parameters. Computer Methods in Applied Mechanics and Engineering, 415. 10.1016/j.cma.2023.116205

Wang, Q., Guillaume, J.H.A., Jakeman, J.D., Bennett, F.R., Croke, B.F.W., Fu, B., Yang, T., Jakeman, A.J., & Jakeman, A.J. (2023). A Decision-Relevant Factor-Fixing Framework: Application to Uncertainty Analysis of a High-Dimensional Water Quality Model. Water Resources Research, 59(8). 10.1029/2022wr032194

White, R.D., Jakeman, J.D., van Bloemen Waanders, B., Kouri, D.P., Alexandarian, A., & Alexandarian, A. (2023). A Bayesian approach to designing experiments that account for risk [Conference Presentation]. 10.2172/2430465

Warner, J., Thompson, M., Geraci, G., Bomarito, G., Eldred, M., Jakeman, J.D., Gorodetsky, A.A., & Gorodetsky, A.A. (2023). Automated Model Tuning for Multifidelity Trajectory Simulation Estimators [Conference Presentation]. 10.2172/2430578

Jakeman, J.D., Geraci, G., & Geraci, G. (2023). Multi-fidelity Surrogate Modeling: A tutorial [Conference Presentation]. 10.2172/2430573

Eldred, M., Geraci, G., Gorodetsky, A.A., Jakeman, J.D., & Jakeman, J.D. (2023). Model Ensemble Configuration for Multifidelity UQ [Conference Presentation]. 10.2172/2430564

Jakeman, J.D., Perego, M., Seidl, D.T., Hillebrand, T., Hoffman, M., Price, S., & Price, S. (2023). Ice Sheet Models of Different Fidelity for Uncertainty Quantification [Conference Presentation]. 10.2172/2430524

Safta, C., Jakeman, J.D., Gorodetsky, A.A., & Gorodetsky, A.A. (2023). Expressive Surrogate Models via Functional Tensor Networks [Conference Presentation]. 10.2172/2431004

Safta, C., Jakeman, J.D., Sargsyan, K., Gorodetsky, A.A., & Gorodetsky, A.A. (2023). Modeling spatio-temporal processes in climate models via functional tensor networks [Conference Presentation]. 10.2172/2431857

Morrow, Z.B., van Bloemen Waanders, B., Jakeman, J.D., & Jakeman, J.D. (2023). RaISE: A Framework to Characterize Surrogate Models in Scientific Machine Learning [Conference Presentation]. 10.2172/2431865

White, R.D., Jakeman, J.D., Wildey, T., & Wildey, T. (2023). Using Data-Consistent Inversion to Build Population-Informed Priors for Bayesian Inference [Conference Presentation]. 10.2172/2540422

Jakeman, J.D., White, R.D., van Bloemen Waanders, B., Alexandarian, A., Kouri, D.P., & Kouri, D.P. (2023). Risk-Averse Goal-Oriented Optimal Experimental Design Using Non-linear Models [Conference Presentation]. 10.2172/2431879

Peterson, K.J., Tezaur, I.K., Powell, A.J., Jakeman, J.D., Roesler, E.L., & Roesler, E.L. (2023). Global Sensitivity Analysis Using the Ultra-Low Resolution E3SM to Investigate Parametric Uncertainty in Arctic Climate [Conference Presentation]. 10.2172/2431840

Zeng, X., Geraci, G., Gorodetsky, A.A., Jakeman, J.D., Ghanem, R., & Ghanem, R. (2023). Improving Bayesian Networks Multifidelity Surrogate Construction with Basis Adaptation [Conference Presentation]. https://doi.org/10.2172/2432101

Zeng, X., Geraci, G., Gorodetsky, A.A., Jakeman, J.D., Eldred, M., Ghanem, R., & Ghanem, R. (2023). Improving Bayesian networks multifidelity surrogate construction with basis adaptation [Conference Presentation]. AIAA SciTech Forum and Exposition, 2023. 10.2172/2432258