Universal Fourier Neural Operators for periodic homogenization problems in linear elasticity
Binh Huy Nguyen, Matti Schneider
TL;DR
This paper shows that Fourier Neural Operators can serve as universal, training-free surrogates for solving cell problems in linear elasticity within FFT-based homogenization. By recasting the Moulinec–Suquet scheme as a neural fixed-point operator and constructing a ReLU-based double-contraction operator, the authors establish uniform fidelity across general microstructures bounded by a material contrast $\kappa$. The method achieves δ-accurate displacement fields and can match FFT solvers in memory and runtime while handling large voxel grids (over $10^8$ voxels); it also demonstrates strong generalization across multiple microstructures, including three distinct composite geometries. This work thus tightly couples FFT-based micromechanics with neural-operator theory, offering a scalable, universal tool for micromechanical analysis with potential extensions to nonlinear or inelastic regimes.
Abstract
Solving cell problems in homogenization is hard, and available deep-learning frameworks fail to match the speed and generality of traditional computational frameworks. More to the point, it is generally unclear what to expect of machine-learning approaches, let alone single out which approaches are promising. In the work at hand, we advocate Fourier Neural Operators (FNOs) for micromechanics, empowering them by insights from computational micromechanics methods based on the fast Fourier transform (FFT). We construct an FNO surrogate mimicking the basic scheme foundational for FFT-based methods and show that the resulting operator predicts solutions to cell problems with arbitrary stiffness distribution only subject to a material-contrast constraint up to a desired accuracy. In particular, there are no restrictions on the material symmetry like isotropy, on the number of phases and on the geometry of the interfaces between materials. Also, the provided fidelity is sharp and uniform, providing explicit guarantees leveraging our physical empowerment of FNOs. To show the desired universal approximation property, we construct an FNO explicitly that requires no training to begin with. Still, the obtained neural operator complies with the same memory requirements as the basic scheme and comes with runtimes proportional to classical FFT solvers. In particular, large-scale problems with more than 100 million voxels are readily handled. The goal of this work is to underline the potential of FNOs for solving micromechanical problems, linking FFT-based methods to FNOs. This connection is expected to provide a fruitful exchange between both worlds.