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arXiv Machine Learning By Zhen Zhang, Alessandro Alla, George Em Karniadakis

Adjoint Method versus Physics-Informed Neural Networks in PDE-Constrained Inverse Problems

Read the original on arXiv Machine Learning →

arXiv:2606. 12337v1 Announce Type: cross Abstract: Inverse problems governed by partial differential equations (PDEs) are central to computational mechanics and are commonly solved by adjoint-based optimization, while physics-informed neural networks (PINNs) have emerged as a flexible alternative.

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

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