Adaptive Importance Sampling for Deep Ritz
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Publisher
Springer Science and Business Media LLC
Link
https://link.springer.com/content/pdf/10.1007/s42967-024-00422-w.pdf
Reference19 articles.
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2. Chen, J., Du, R., Wu, K.: A comparison study of deep Galerkin method and deep Ritz method for elliptic problems with different boundary conditions. arXiv:2005.04554 (2020)
3. E, W.N., Yu, B.: The deep Ritz method: a deep learning-based numerical algorithm for solving variational problems. Commun. Math. Stat. 6(1), 1–12 (2018)
4. Gao, Z., Tang, T., Yan, L., Zhou, T.: Failure-informed adaptive sampling for PINNs, part II: combining with re-sampling and subset simulation. Commun. Appl. Math. Comput., (2023). https://doi.org/10.1007/s42967-023-00312-7
5. Gao, Z., Yan, L., Zhou, T.: Failure-informed adaptive sampling for PINNs. SIAM J. Sci. Comput. 45(4), 1971–1994 (2023)
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