The internal law of a material can be discovered from its boundary

TL;DR

Neural-DFEM is a novel method that enables unsupervised discovery of hyperelastic material laws from partial boundary measurements by embedding a differentiable finite element solver and enforcing physical constraints.

Contribution

It introduces Hyperelastic Neural Networks and a boundary-only measurement framework for robust, physically consistent material law identification in 2D and 3D.

Findings

Achieves high accuracy in material law discovery from boundary data.

Demonstrates robustness to measurement noise.

Generalizes across different geometries and loading conditions.

Abstract

Since the earliest stages of human civilization, advances in technology have been tightly linked to our ability to understand and predict the mechanical behavior of materials. In recent years, this challenge has increasingly been framed within the broader paradigm of data-driven scientific discovery, where governing laws are inferred directly from observations. However, existing methods require either stress-strain pairs or full-field displacement measurements, which are often inaccessible in practice. We introduce Neural-DFEM, a method that enables unsupervised discovery of hyperelastic material laws even from partial observations, such as boundary-only measurements. The method embeds a differentiable finite element solver within the learning loop, directly linking candidate energy functionals to available measurements. To guarantee thermodynamic consistency and mathematical well-posedness throughout training, the method employs Hyperelastic Neural Networks, a novel structure-preserving neural architecture that enforces frame indifference, material symmetry, polyconvexity, and coercivity by design. The resulting framework enables robust material model discovery in both two- and three-dimensional settings, including scenarios with boundary-only measurements. Neural-DFEM allows for generalization across geometries and loading conditions, and exhibits unprecedented accuracy and strong resilience to measurement noise. Our results demonstrate that reliable identification of material laws is achievable even under partial observability when strong physical inductive biases are embedded in the learning architecture.