GIMLET: Generalizable and Interpretable Model Learning through Embedded Thermodynamics

TL;DR

GIMLET is a thermodynamics-based neural network framework that discovers constitutive relations in fluid and scalar transport models, ensuring physical consistency and transferability across datasets.

Contribution

It introduces a thermodynamics-informed neural network approach that enforces physical laws without predefined function libraries, enabling interpretable and generalizable model discovery.

Findings

Successfully applied to Burgers, Kuramoto--Sivashinsky, and Navier--Stokes equations.

Ensures thermodynamic consistency through energy and entropy principles.

Models transfer across different datasets governed by the same physics.

Abstract

We develop a data-driven framework for discovering constitutive relations in models of fluid flow and scalar transport. Under the assumption that velocity and/or scalar fields are measured, our approach infers unknown closure terms in the governing equations as neural networks. The target to be discovered is the constitutive relations only, while the temporal derivative, convective transport terms, and pressure-gradient term in the governing equations are prescribed. The formulation is rooted in a variational principle from non-equilibrium thermodynamics, where the dynamics is defined by a free-energy functional and a dissipation functional. The unknown constitutive terms arise as functional derivatives of these functionals with respect to the state variables. To enable a flexible and structured model discovery, the free-energy and dissipation functionals are parameterized using neural networks, while their functional derivatives are obtained via automatic differentiation. This construction enforces thermodynamic consistency by design, guaranteeing monotonic decay of the total free energy and non-negative entropy production. The resulting method, termed GIMLET (Generalizable and Interpretable Model Learning through Embedded Thermodynamics), avoids reliance on a predefined library of candidate functions, unlike sparse regression or symbolic identification approaches. The learned models are generalizable in that functionals identified from one dataset can be transferred to distinct datasets governed by the same underlying equations. Moreover, the inferred free-energy and dissipation functions provide direct physical interpretability of the learned dynamics. The framework is demonstrated on several benchmark systems, including the viscous Burgers equation, the Kuramoto--Sivashinsky equation, and the incompressible Navier--Stokes equations for both Newtonian and non-Newtonian fluids.