Failure-Informed Adaptive Sampling for PINNs, Part II: Combining with Re-sampling and Subset Simulation

Author:

Gao Zhiwei,Tang Tao,Yan Liang,Zhou Tao

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Applied Mathematics

Reference21 articles.

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2. Bischof, R., Kraus, M.: Multi-objective loss balancing for physics-informed deep learning. arXiv:2110.09813 (2021)

3. Daw, A., Bu, J., Wang, S., Perdikaris, P., Karpatne, A.: Rethinking the importance of sampling in physics-informed neural networks. arXiv:2207.02338 (2022)

4. E, W.N.: Machine learning and computational mathematics. Commun. Comput. Phys. 28, 1639–1670 (2020)

5. E, W.N., Yu, B.: The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems. Commun. Math. Stat. 6, 1–12 (2018)

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