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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.

, , , and . CoRR, (2021)

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Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems., , , and . CoRR, (2021)NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators., , , and . SIAM Rev., 66 (1): 161-190 (February 2024)Variational inference in neural functional prior using normalizing flows: Application to differential equation and operator learning problems.. CoRR, (2023)Uncertainty quantification for noisy inputs-outputs in physics-informed neural networks and neural operators., , and . CoRR, (2023)PPINN: Parareal Physics-Informed Neural Network for time-dependent PDEs., , , and . CoRR, (2019)B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data., , and . J. Comput. Phys., (2021)Physics-informed neural networks for predicting gas flow dynamics and unknown parameters in diesel engines., , , and . CoRR, (2023)NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators., , , and . CoRR, (2022)Uncertainty Quantification in Scientific Machine Learning: Methods, Metrics, and Comparisons., , , , and . CoRR, (2022)DeepXDE: A Deep Learning Library for Solving Differential Equations., , , and . AAAI Spring Symposium: MLPS, volume 2587 of CEUR Workshop Proceedings, CEUR-WS.org, (2020)