Learning Parameterized Nonlinear Elasticity on Curved Surfaces

TL;DR

This paper introduces a physics-informed neural network that learns nonlinear elasticity on curved surfaces, enabling efficient modeling across various geometries and material parameters without reinitialization.

Contribution

The authors develop a neural network framework that enforces elasticity physics directly in the loss function, allowing for continuous representation of elastic equilibria on curved manifolds.

Findings

The network accurately reproduces known solutions for a disclination on a spheroidal surface.

It generalizes well to parameter combinations not seen during training.

The method offers a scalable approach for complex curved elastic systems.

Abstract

We learn parameterized nonlinear elasticity on curved surfaces using a physics-informed neural network that enforces governing equations and boundary conditions directly through the loss function, enabling a single trained model to represent a continuous family of elastic equilibria across geometric and material parameters. Nonlinear elasticity on curved manifolds underlies the mechanics of crystalline shells, elastic membranes, and viral capsids, where curvature and topological defects determine equilibrium structure and stability. Traditional exact and finite element solvers rely on symmetry reduction and must be reinitialized for each parameter choice, limiting scalability when symmetry is broken or parameters vary. We validate the proposed learning-based solver on a benchmark problem from curved elasticity, namely the one-dimensional single disclination on a spheroidal surface with known exact and numerical solutions. The network accurately reproduces these solutions, including parameter combinations excluded from training, demonstrating generalization across geometry and material regimes. This study establishes a scalable framework for learning nonlinear elastic systems on curved manifolds and lays the groundwork for extensions to fully two-dimensional and multi-defect configurations relevant to protein shells and other curved elastic networks.