WGFINNs: Weak formulation-based GENERIC formalism informed neural networks
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. To address this limitation, we propose weak formulation-based GENERIC formalism informed neural networks (WGFINNs), which integrate the weak formulation of dynamical systems with the structure-preserving architecture of GFINNs. WGFINNs significantly enhance robustness to noisy data while retaining exact satisfaction o...
| Authors | Jun Sur Richard Park, Auroni Huque Hashim, Siu Wun Cheung et al. |
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| Primary category | cs.LG |
| Published | 3 April 2026 |
| Last updated at source | 7 April 2026 |
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