Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces
This addresses the challenge of designing tailored frictional interfaces for applications like robotics or materials science, though it appears incremental as it builds on existing generative methods for a specific domain.
The paper tackled the inverse design of frictional interfaces to achieve prescribed macroscopic behavior by introducing a generative modeling framework using Variational Autoencoders, which enabled efficient, simulation-free generation of candidate surface topographies from a synthetic dataset of 200 million samples.
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.