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Abstract <p>A method of solving inverse coefficient problems for a thermoelastic layer is proposed on the basis of a physics-informed neural network (PINN). The classical thermoelasticity equations are adopted, with appropriate initial and boundary conditions; observable data are employed. A loss function allows the remainders of the equations and the initial and boundary conditions to be taken into account, along with errors in the specified data. Numerical analysis of a model problem indicates high accuracy of the method in recovering unknown coefficients of the material. The PINN method proves effective and flexible in solving complex continuum mechanics problems.</p>

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Solution of Nonsteady Inverse Coefficient Problems for a Thermoelastic Layer by Machine Learning

  • 期刊论文

  • 发表日期：

    2025年4月1日

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  • V. A. Vestyak,
  • A. Yu. Ershova,
  • Phan Tung Son,
  • G. V. Fedotenkov

摘要

Abstract

A method of solving inverse coefficient problems for a thermoelastic layer is proposed on the basis of a physics-informed neural network (PINN). The classical thermoelasticity equations are adopted, with appropriate initial and boundary conditions; observable data are employed. A loss function allows the remainders of the equations and the initial and boundary conditions to be taken into account, along with errors in the specified data. Numerical analysis of a model problem indicates high accuracy of the method in recovering unknown coefficients of the material. The PINN method proves effective and flexible in solving complex continuum mechanics problems.