Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities.

ABSTRACT: Standard physics informed deep learning and their enhanced variants encounter challenges in addressing inverse problems characterized by extreme discontinuities and high-order parameterized differential equations due to the use of globally smooth activation functions, especially when the unknown parameters exhibit spatially distributed characteristics. Phenomena such as discontinuous loads, boundary truncations, and abrupt changes in material properties introduce singularities in the derivatives, which in turn lead to ill-conditioned information in the gradient flow. To address these limitations, here we propose an information-distilled physics-informed deep-learning framework that combines reduced-order modeling, multi-level domain decomposition, and an ill-conditioning-suppression mechan

SUBMITTER: Peng M 

PROVIDER: S-EPMC12402147 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature