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Found 385 Documents (Results 1–100)

Operator Learning Enhanced Physics-informed Neural Networks for Solving Partial Differential Equations Characterized by Sharp Solutions. arXiv:2310.19590

Preprint, arXiv:2310.19590 [cs.LG] (2023).
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Enhancing training of physics-informed neural networks using domain-decomposition based preconditioning strategies. arXiv:2306.17648

Preprint, arXiv:2306.17648 [math.NA] (2023).
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Deep neural operators can serve as accurate surrogates for shape optimization: A case study for airfoils. arXiv:2302.00807

Preprint, arXiv:2302.00807 [physics.flu-dyn] (2023).
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A Hybrid Iterative Numerical Transferable Solver (HINTS) for PDEs Based on Deep Operator Network and Relaxation Methods. arXiv:2208.13273

Preprint, arXiv:2208.13273 [math.NA] (2022).
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Fractional calculus and numerical methods for fractional PDEs. (English) Zbl 1442.35514

Emmanouil, Ioannis (ed.) et al., First congress of Greek mathematicians. Proceedings of the congress held in Athens, Greece, June 25–30, 2018. Berlin: De Gruyter. De Gruyter Proc. Math., 91-125 (2020).
MSC:  35R11 65M70
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