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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)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)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)Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries., , and . Neural Comput., 36 (3): 475-498 (March 2024)Δ-PINNs: Physics-informed neural networks on complex geometries., , and . Eng. Appl. Artif. Intell., 127 (Part B): 107324 (January 2024)Improved architectures and training algorithms for deep operator networks., , and . CoRR, (2021)