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arXiv Machine Learning By Yongjin Choi, Hyeonbin Moon, Seunghwa Ryu

Critical evaluation of PINN for FWD inverse analysis and differentiable FEM as an alternative

Read the original on arXiv Machine Learning →

arXiv:2606. 03210v1 Announce Type: cross Abstract: Automatic-differentiation-based inverse analysis methods, including physics-informed neural networks (PINNs) and differentiable programming, have recently shown great promise due to their ability to compute accurate gradients and convergence efficiency.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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