arXiv Machine Learning June 5, 2026 By Cornelius Otchere, Michael Shields

Effective Dimensionality as an Operator Invariant for Physics-Preserving Constraint Adaptation in Physics-Informed Neural Networks

Read the original on arXiv Machine Learning →

arXiv:2606. 06171v1 Announce Type: cross Abstract: Physics-Informed Neural Networks inherently suffer from task interference because they rely on a shared parameter space to satisfy both governing differential equations and boundary conditions.

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

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Physics-Informed Neural Embeddings of PDE Solution Families

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