AI RESEARCH
WGFINNs: Weak formulation-based GENERIC formalism informed neural networks'
arXiv CS.LG
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ArXi:2604.02601v1 Announce Type: new Data-driven discovery of governing equations from noisy observations remains a fundamental challenge in scientific machine learning. While GENERIC formalism informed neural networks (GFINNs) provide a principled framework that enforces the laws of thermodynamics by construction, their reliance on strong-form loss formulations makes them highly sensitive to measurement noise.