[2603.26803] A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks

Computer Science > Machine Learning arXiv:2603.26803 (cs) [Submitted on 26 Mar 2026] Title:A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks Authors:Guojie Li, Liu Hong View a PDF of the paper titled A Comparative Investigation of Thermodynamic Structure-Informed Neural Networks, by Guojie Li and 1 other authors View PDF HTML (experimental) Abstract:Physics-informed neural networks (PINNs) offer a unified framework for solving both forward and inverse problems of differential equations, yet their performance and physical consistency strongly depend on how governing laws are incorporated. In this work, we present a systematic comparison of different thermodynamic structure-informed neural networks by incorporating various thermodynamics formulations, including Newtonian, Lagrangian, and Hamiltonian mechanics for conservative systems, as well as the Onsager variational principle and extended irreversible thermodynamics for dissipative systems. Through comprehensive numerical experiments on representative ordinary and partial differential equations, we quantitatively evaluate the impact of these formulations on accuracy, physical consistency, noise robustness, and interpretability. The results show that Newtonian-residual-based PINNs can reconstruct system states but fail to reliably recover key physical and thermodynamic quantities, whereas structure-preserving formulation significantly enhances parameter identification, thermodynamic consistenc...