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

• URL: http://arxiv.org/abs/2511.03735v1
• Date: Mon, 27 Oct 2025 13:00:48 GMT
• Title: Friction on Demand: A Generative Framework for the Inverse Design of Metainterfaces
• Authors: Valentin Mouton, Adrien Mélot,
• Abstract summary: We introduce a generative modeling framework to infer surface topographies from target friction laws.<n> 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.<n>This approach paves the way for near-real-time control of frictional behavior through tailored surface topographies.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

Related papers

• Interpretable Robotic Friction Learning via Symbolic Regression [52.41267112707149]
  Accurately modeling the friction torque in robotic joints has long been challenging due to the request for a robust mathematical description.<n>Traditional model-based approaches are often labor-intensive, requiring extensive experiments and expert knowledge.<n>Data-driven methods based on neural networks are easier to implement but often lack robustness.
  arXiv  Detail & Related papers  (2025-05-19T14:44:02Z)
• Learning Transferable Friction Models and LuGre Identification via Physics Informed Neural Networks [4.432363122731454]
  We present a physics-informed friction estimation framework to integrate well-established friction models with learnable components.<n>Our approach enforces physical consistency yet retains the flexibility to adapt to real-world complexities.<n>We show that our approach enables the learned models to be transferable to systems they are not trained on.
  arXiv  Detail & Related papers  (2025-04-16T19:15:48Z)
• Training-Free Constrained Generation With Stable Diffusion Models [41.391765899175276]
  Stable diffusion models represent the state-of-the-art in data synthesis across diverse domains.<n>Existing techniques are either limited in their applicability to latent diffusion frameworks or lack the capability to strictly enforce domain-specific constraints.<n>This paper proposes a novel integration of stable diffusion models with constrained optimization frameworks, enabling the generation of outputs satisfying stringent physical and functional requirements.
  arXiv  Detail & Related papers  (2025-02-08T16:11:17Z)
• Simultaneous Multi-Robot Motion Planning with Projected Diffusion Models [57.45019514036948]
  Simultaneous MRMP Diffusion (SMD) is a novel approach integrating constrained optimization into the diffusion sampling process to produce collision-free, kinematically feasible trajectories.<n>The paper introduces a comprehensive MRMP benchmark to evaluate trajectory planning algorithms across scenarios with varying robot densities, obstacle complexities, and motion constraints.
  arXiv  Detail & Related papers  (2025-02-05T20:51:28Z)
• Constrained Synthesis with Projected Diffusion Models [47.56192362295252]
  This paper introduces an approach to generative diffusion processes the ability to satisfy and certify compliance with constraints and physical principles. The proposed method recast the traditional process of generative diffusion as a constrained distribution problem to ensure adherence to constraints.
  arXiv  Detail & Related papers  (2024-02-05T22:18:16Z)
• Transfer learning-assisted inverse modeling in nanophotonics based on mixture density networks [0.840835093659811]
  In this paper, we propose an inverse modeling method for nanophotonic structures based on a mixture density network model enhanced by transfer learning. The proposed approach allows overcoming these limitations using transfer learning-based techniques, while preserving a high accuracy in the prediction capability of the design solutions given an optical response as an input.
  arXiv  Detail & Related papers  (2024-01-21T09:03:30Z)
• Learning-based adaption of robotic friction models [50.72489248401199]
  We introduce a novel approach to adapt an existing friction model to new dynamics using as little data as possible.<n>Our method does not rely on data with external load during training, eliminating the need for external torque sensors.
  arXiv  Detail & Related papers  (2023-10-25T14:50:15Z)
• INFINITY: Neural Field Modeling for Reynolds-Averaged Navier-Stokes Equations [13.242926257057084]
  INFINITY is a deep learning model that encodes geometric information and physical fields into compact representations. Our framework achieves state-of-the-art performance by accurately inferring physical fields throughout the volume and surface. Our model can correctly predict drag and lift coefficients while adhering to the equations.
  arXiv  Detail & Related papers  (2023-07-25T14:35:55Z)
• DiMSam: Diffusion Models as Samplers for Task and Motion Planning under Partial Observability [58.75803543245372]
  Task and Motion Planning (TAMP) approaches are suited for planning multi-step autonomous robot manipulation. We propose to overcome these limitations by composing diffusion models using a TAMP system. We show how the combination of classical TAMP, generative modeling, and latent embedding enables multi-step constraint-based reasoning.
  arXiv  Detail & Related papers  (2023-06-22T20:40:24Z)
• Approximating Constraint Manifolds Using Generative Models for Sampling-Based Constrained Motion Planning [8.924344714683814]
  This paper presents a learning-based sampling strategy for constrained motion planning problems. We use Conditional Variversaational Autoencoder (CVAE) and Conditional Generative Adrial Net (CGAN) to generate constraint-satisfying sample configurations. We evaluate the efficiency of these two generative models in terms of their sampling accuracy and coverage of sampling distribution.
  arXiv  Detail & Related papers  (2022-04-14T07:08:30Z)
• Hybridized Methods for Quantum Simulation in the Interaction Picture [69.02115180674885]
  We provide a framework that allows different simulation methods to be hybridized and thereby improve performance for interaction picture simulations. Physical applications of these hybridized methods yield a gate complexity scaling as $log2 Lambda$ in the electric cutoff. For the general problem of Hamiltonian simulation subject to dynamical constraints, these methods yield a query complexity independent of the penalty parameter $lambda$ used to impose an energy cost.
  arXiv  Detail & Related papers  (2021-09-07T20:01:22Z)
• Congestion-aware Multi-agent Trajectory Prediction for Collision Avoidance [110.63037190641414]
  We propose to learn congestion patterns explicitly and devise a novel "Sense--Learn--Reason--Predict" framework. By decomposing the learning phases into two stages, a "student" can learn contextual cues from a "teacher" while generating collision-free trajectories. In experiments, we demonstrate that the proposed model is able to generate collision-free trajectory predictions in a synthetic dataset.
  arXiv  Detail & Related papers  (2021-03-26T02:42:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.