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An efficient epistemic uncertainty quantification algorithm for a class of stochastic models: A post-processing and domain decomposition framework., , , and . CoRR, (2020)A comparative study of physics-informed neural network models for learning unknown dynamics and constitutive relations., , , and . CoRR, (2019)Basis adaptation and domain decomposition for steady-state partial differential equations with random coefficients., , and . J. Comput. Phys., (2017)Conditional Korhunen-Loéve regression model with Basis Adaptation for high-dimensional problems: uncertainty quantification and inverse modeling., , , and . CoRR, (2023)Accelerating Scientific Simulations with Bi-Fidelity Weighted Transfer Learning., , , , , , , and . ICMLA, page 994-999. IEEE, (2023)Physics-informed Machine Learning Method for Forecasting and Uncertainty Quantification of Partially Observed and Unobserved States in Power Grids., and . HICSS, page 1-7. ScholarSpace, (2019)Basis adaptation in homogeneous chaos spaces., and . J. Comput. Phys., (2014)Conditional Karhunen-Loève expansion for uncertainty quantification and active learning in partial differential equation models., , and . J. Comput. Phys., (2020)Extending Conformal Prediction to Hidden Markov Models with Exact Validity via de Finetti's Theorem for Markov Chains., , , and . CoRR, (2022)FPDetect: Efficient Reasoning About Stencil Programs Using Selective Direct Evaluation., , , , and . ACM Trans. Archit. Code Optim., 17 (3): 19:1-19:27 (2020)