用生成模型反向设计表面纹理，实现快速调控摩擦力。
Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces
- 基于变分自编码器，从目标摩擦特性生成对应表面结构
- 训练数据达2亿样本，实现无仿真快速生成候选结构
- 适合需要快速定制摩擦特性的工程与材料设计场景
设计能实现特定宏观摩擦行为的界面是一项具有挑战性的逆向问题，受限于解的非唯一性以及接触模拟的高计算成本。传统方法依赖低维参数化的启发式搜索，难以应对复杂或非线性摩擦规律。本文提出一种基于变分自编码器（VAEs）的生成建模框架，从目标摩擦律推断表面形貌。模型在由参数化接触力学模型构建的2亿样本合成数据集上训练，实现无需仿真的高效候选形貌生成。我们探讨了生成建模在此逆向设计任务中的潜力与局限，重点关注生成结果在精度、吞吐量和多样性之间的权衡。实验揭示了各目标间的取舍关系，并给出了实际应用中的关键考量。该方法为通过定制表面形貌实现近实时摩擦调控提供了新路径。
原文摘要 · Abstract (English)
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.
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