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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

, , and . Journal of Computational physics, (2019)

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Physics-informed machine learning, , , , , and . Nature Reviews Physics, 3 (6): 422--440 (2021)Inferring solutions of differential equations using noisy multi-fidelity data., , and . CoRR, (2016)Random Weight Factorization Improves the Training of Continuous Neural Representations., , , and . CoRR, (2022)Semi-supervised Invertible DeepONets for Bayesian Inverse Problems., , and . CoRR, (2022)Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks., , , , and . CoRR, (2023)Understanding and mitigating gradient pathologies in physics-informed neural networks., , and . CoRR, (2020)Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data., , , and . J. Comput. Phys., (2019)Conditional deep surrogate models for stochastic, high-dimensional, and multi-fidelity systems., and . CoRR, (2019)Multi-fidelity Gaussian process regression for prediction of random fields., , , and . J. Comput. Phys., (2017)Bayesian differential programming for robust systems identification under uncertainty., , and . CoRR, (2020)