Constitutive Priors for Inverse Design

TL;DR

This paper presents a novel end-to-end inverse design framework for elastic networks, leveraging a constitutive prior from noisy data, PDE-constrained optimization, and advanced regularization techniques.

Contribution

It introduces a new approach combining a latent manifold of material laws, homotopy continuation, and neural-network-based smoothness priors for robust inverse design.

Findings

Successfully applied to elastic network design problems.

Outperforms alternative optimization strategies.

Ensures thermodynamic consistency and manufacturability constraints.

Abstract

This work introduces an end-to-end framework for inverse design of elastic networks directly in the space of constitutive behaviors. A constitutive prior is constructed from noisy stress-strain data using a latent representation that defines a manifold of admissible material laws while enforcing thermodynamic consistency. The inverse problem is formulated as a PDE-constrained optimization problem over latent constitutive variables that parameterize spatially varying material behavior. To improve robustness in the resulting nonconvex optimization, a homotopy-based continuation strategy is introduced using intermediate target point clouds generated through affine registration. Geometry matching is performed using the Chamfer distance, enabling optimization without requiring mesh correspondence between the target and reference configurations. To account for manufacturing constraints limiting abrupt spatial variation in material properties, the framework additionally incorporates a neural-network-based smoothness prior together with a graph-based smoothness metric. The proposed approach is demonstrated on several inverse design problems for elastic networks and compared against alternative optimization strategies.