AI RESEARCH
A Residual Guided strategy with Generative Adversarial Networks in training Physics-Informed Transformer Networks
arXiv CS.LG
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ArXi:2508.00855v2 Announce Type: replace Nonlinear partial differential equations (PDEs) are pivotal in modeling complex physical systems, yet traditional Physics-Informed Neural Networks (PINNs) often struggle with unresolved residuals in critical spatiotemporal regions and violations of temporal causality. To address these limitations, we propose a novel Residual Guided