Physics-informed neural network framework for solving forward and inverse flexoelectric problems

TL;DR

This paper introduces a physics-informed neural network framework based on an energy formulation to efficiently solve complex flexoelectric problems, including both forward and inverse cases, with high accuracy and stability.

Contribution

It develops a unified PINN approach using energy-based formulation and deep energy method to handle high-order PDEs in flexoelectricity, enabling stable inverse parameter identification.

Findings

Accurately predicts flexoelectric responses matching mixed-FEM solutions.

Successfully recovers unknown material coefficients from limited data.

Demonstrates stability and scalability of the PINN framework for high-order problems.

Abstract

Flexoelectricity, the coupling between strain gradients and electric polarization, poses significant computational challenges due to its governing fourth-order partial differential equations that require C1-continuous solutions. To address these issues, we propose a physics-informed neural network (PINN) framework grounded in an energy-based formulation that treats both forward and inverse problems within a unified architecture. The forward problem is recast as a saddle-point optimization of the total potential energy, solved via the deep energy method (DEM), which circumvents the direct computation of high-order derivatives. For the inverse problem of identifying unknown flexoelectric coefficients from sparse measurements, we introduce an additional variational loss that enforces stationarity with respect to the electric potential, ensuring robust and stable parameter inference. The framework integrates finite element-based numerical quadrature for stable energy evaluation and employs hard constraints to rigorously enforce boundary conditions. Numerical results for both direct and converse flexoelectric effects show excellent agreement with mixed-FEM solutions, and the inverse model accurately recovers material parameters from limited data. This study establishes a unified, mesh-compatible, and scalable PINN approach for high-order electromechanical problems, offering a promising alternative to traditional simulation techniques.