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DeepXDE: A deep learning library for solving differential equations

, , , and . SIAM Review, 63 (1): 208--228 (2021)

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Multi-fidelity Bayesian neural networks: Algorithms and applications., , and . J. Comput. Phys., (2021)Physics-informed neural networks with residual/gradient-based adaptive sampling methods for solving PDEs with sharp solutions., and . CoRR, (2023)Correcting model misspecification in physics-informed neural networks (PINNs)., , and . CoRR, (2023)Deep neural operator for learning transient response of interpenetrating phase composites subject to dynamic loading., , , , , and . CoRR, (2023)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)NeuralUQ: A Comprehensive Library for Uncertainty Quantification in Neural Differential Equations and Operators., , , and . SIAM Rev., 66 (1): 161-190 (February 2024)NeuralUQ: A comprehensive library for uncertainty quantification in neural differential equations and operators., , , 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)B-PINNs: Bayesian physics-informed neural networks for forward and inverse PDE problems with noisy data., , and . J. Comput. Phys., (2021)