Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces

TL;DR

This paper presents a generative framework using Variational Autoencoders to efficiently design surface topographies for desired frictional behaviors, overcoming traditional computational challenges in inverse contact mechanics problems.

Contribution

The authors introduce a novel VAE-based method trained on a large synthetic dataset to enable fast, simulation-free inverse design of frictional interfaces with complex laws.

Findings

Enables rapid generation of candidate topographies for target friction laws

Balances accuracy, diversity, and computational efficiency in design

Highlights practical trade-offs in generative inverse design

Abstract

Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits their applicability to more complex or nonlinear friction laws. We introduce a generative modeling framework using Variational Autoencoders (VAEs) to infer surface topographies from target friction laws. Trained on a synthetic dataset composed of 200 million samples constructed from a parameterized contact mechanics model, the proposed method enables efficient, simulation-free generation of candidate topographies. We examine the potential and limitations of generative modeling for this inverse design task, focusing on balancing accuracy, throughput, and diversity in the generated solutions. Our results highlight trade-offs and outline practical considerations when balancing these objectives. This approach paves the way for near-real-time control of frictional behavior through tailored surface topographies.