LNN-PINN: A Unified Physics-Only Training Framework with Liquid Residual Blocks

arXiv:2508.08935v4 Announce Type: cross Abstract: Physics-informed neural networks (PINNs) have attracted considerable attention for their ability to integrate partial differential equation priors into deep learning frameworks; however, they often exhibit limited predictive accuracy when applied to complex problems. To address this issue, we propose LNN-PINN, a physics-informed neural network framework that incorporates a liquid residual gating architecture while preserving the original physics modeling and optimization pipeline to improve predictive accuracy. The method introduces a lightweig

Source: arXiv cs.AI — read the full report at the original publisher.

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