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Ensemble learning for Physics Informed Neural Networks: a Gradient Boosting approach.

, , и . CoRR, (2023)

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Georgios Perdikaris

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Inferring solutions of differential equations using noisy multi-fidelity data., , и . CoRR, (2016)Learning Only on Boundaries: A Physics-Informed Neural Operator for Solving Parametric Partial Differential Equations in Complex Geometries., , и . Neural Comput., 36 (3): 475-498 (марта 2024)Physics-informed machine learning, , , , , и . Nature Reviews Physics, 3 (6): 422--440 (2021)Scalable Bayesian optimization with high-dimensional outputs using randomized prior networks., , , , и . CoRR, (2023)Semi-supervised Invertible DeepONets for Bayesian Inverse Problems., , и . CoRR, (2022)Rethinking the Importance of Sampling in Physics-informed Neural Networks., , , , и . CoRR, (2022)Bayesian differential programming for robust systems identification under uncertainty., , и . CoRR, (2020)Δ-PINNs: Physics-informed neural networks on complex geometries., , и . Eng. Appl. Artif. Intell., 127 (Part B): 107324 (января 2024)Mitigating Propagation Failures in Physics-informed Neural Networks using Retain-Resample-Release (R3) Sampling., , , , и . ICML, том 202 из Proceedings of Machine Learning Research, стр. 7264-7302. PMLR, (2023)Learning Atrial Fiber Orientations and Conductivity Tensors from Intracardiac Maps Using Physics-Informed Neural Networks., , , , , , и . FIMH, том 12738 из Lecture Notes in Computer Science, стр. 650-658. Springer, (2021)