Equation Discovery, Parametric Simulation, and Optimization Using the Physics-Informed Neural Network (PINN) Method for the Heat Conduction Problem

TL;DR

This paper explores the use of Physics-Informed Neural Networks (PINNs) for modeling, simulation, and optimization in heat conduction problems, emphasizing equation discovery, parameter analysis, and inverse problem solving.

Contribution

It introduces a comprehensive framework applying PINNs to discover equations, perform parametric simulations, and solve inverse problems in heat conduction, highlighting their versatility over traditional methods.

Findings

PINNs successfully reconstructed fractional heat transfer equations.

The method effectively analyzed thermal conductivity influence.

PINNs provided valuable solutions for inverse property estimation.

Abstract

In this study, the capabilities of the Physics-Informed Neural Network (PINN) method are investigated for three major tasks: modeling, simulation, and optimization in the context of the heat conduction problem. In the modeling phase, the governing equation of heat transfer by conduction is reconstructed through equation discovery using fractional-order derivatives, enabling the identification of the fractional derivative order that best describes the physical behavior. In the simulation phase, the thermal conductivity is treated as a physical parameter, and a parametric simulation is performed to analyze its influence on the temperature field. In the optimization phase, the focus is placed on the inverse problem, where the goal is to infer unknown physical properties from observed data. The effectiveness of the PINN approach is evaluated across these three fundamental engineering problem types and compared against conventional numerical methods. The results demonstrate that although PINNs may not yet outperform traditional numerical solvers in terms of speed and accuracy for forward problems, they offer a powerful and flexible framework for parametric simulation, optimization, and equation discovery, making them highly valuable for inverse and data-driven modeling applications.