Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning

• URL: http://arxiv.org/abs/2511.05234v1
• Date: Fri, 07 Nov 2025 13:34:02 GMT
• Title: Context-aware Learned Mesh-based Simulation via Trajectory-Level Meta-Learning
• Authors: Philipp Dahlinger, Niklas Freymuth, Tai Hoang, Tobias Würth, Michael Volpp, Luise Kärger, Gerhard Neumann,
• Abstract summary: Learned Graph Network Simulators (GNSs) offer a promising alternative to traditional mesh-based physics simulators.<n>Their speed and inherent differentiability make them well suited for applications that require fast and accurate simulations, such as robotic manipulation or manufacturing optimization.<n>We utilize movement primitives to directly predict fast, stable and accurate simulations from a single model call.
• Score: 20.72669976554826
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating object deformations is a critical challenge across many scientific domains, including robotics, manufacturing, and structural mechanics. Learned Graph Network Simulators (GNSs) offer a promising alternative to traditional mesh-based physics simulators. Their speed and inherent differentiability make them particularly well suited for applications that require fast and accurate simulations, such as robotic manipulation or manufacturing optimization. However, existing learned simulators typically rely on single-step observations, which limits their ability to exploit temporal context. Without this information, these models fail to infer, e.g., material properties. Further, they rely on auto-regressive rollouts, which quickly accumulate error for long trajectories. We instead frame mesh-based simulation as a trajectory-level meta-learning problem. Using Conditional Neural Processes, our method enables rapid adaptation to new simulation scenarios from limited initial data while capturing their latent simulation properties. We utilize movement primitives to directly predict fast, stable and accurate simulations from a single model call. The resulting approach, Movement-primitive Meta-MeshGraphNet (M3GN), provides higher simulation accuracy at a fraction of the runtime cost compared to state-of-the-art GNSs across several tasks.

Related papers

• D-REX: Differentiable Real-to-Sim-to-Real Engine for Learning Dexterous Grasping [66.22412592525369]
  We introduce a real-to-sim-to-real engine that leverages the Gaussian Splat representations to build a differentiable engine.<n>We show that our engine achieves accurate and robust performance in mass identification across various object geometries and mass values.<n>Those optimized mass values facilitate force-aware policy learning, achieving superior and high performance in object grasping.
  arXiv  Detail & Related papers  (2026-03-01T15:32:04Z)
• MaNGO - Adaptable Graph Network Simulators via Meta-Learning [25.80703650406406]
  Graph Network Simulators (GNSs) offer faster inference but suffer from two key limitations.<n>They must be retrained from scratch for even minor variations in physical parameters.<n>This is inefficient, as simulations with varying parameters often share a common underlying latent structure.<n>We propose a novel architecture that generates a latent representation by encoding graph trajectories using conditional neural processes.
  arXiv  Detail & Related papers  (2025-10-07T12:44:24Z)
• Hybrid Neural-MPM for Interactive Fluid Simulations in Real-Time [57.30651532625017]
  We present a novel hybrid method that integrates numerical simulation, neural physics, and generative control.<n>Our system demonstrates robust performance across diverse 2D/3D scenarios, material types, and obstacle interactions.<n>We promise to release both models and data upon acceptance.
  arXiv  Detail & Related papers  (2025-05-25T01:27:18Z)
• GausSim: Foreseeing Reality by Gaussian Simulator for Elastic Objects [55.02281855589641]
  GausSim is a novel neural network-based simulator designed to capture the dynamic behaviors of real-world elastic objects represented through Gaussian kernels.<n>We leverage continuum mechanics and treat each kernel as a Center of Mass System (CMS) that represents continuous piece of matter.<n>In addition, GausSim incorporates explicit physics constraints, such as mass and momentum conservation, ensuring interpretable results and robust, physically plausible simulations.
  arXiv  Detail & Related papers  (2024-12-23T18:58:17Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• Latent Task-Specific Graph Network Simulators [16.881339139068018]
  Graph Network Simulators (GNSs) pose an efficient alternative to traditional physics-based simulators. We frame mesh-based simulation as a meta-learning problem and use a recent Bayesian meta-learning method to improve GNSs adaptability to new scenarios. We validate the effectiveness of our approach through various experiments, performing on par with or better than established baseline methods.
  arXiv  Detail & Related papers  (2023-11-09T10:30:51Z)
• Waymax: An Accelerated, Data-Driven Simulator for Large-Scale Autonomous Driving Research [76.93956925360638]
  Waymax is a new data-driven simulator for autonomous driving in multi-agent scenes. It runs entirely on hardware accelerators such as TPUs/GPUs and supports in-graph simulation for training. We benchmark a suite of popular imitation and reinforcement learning algorithms with ablation studies on different design decisions.
  arXiv  Detail & Related papers  (2023-10-12T20:49:15Z)
• Grounding Graph Network Simulators using Physical Sensor Observations [12.017054986629846]
  We integrate sensory information to ground Graph Network Simulators on real world observations. We predict the mesh state of deformable objects by utilizing point cloud data.
  arXiv  Detail & Related papers  (2023-02-23T09:06:42Z)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• Constraint-based graph network simulator [9.462808515258464]
  We present a framework for constraint-based learned simulation. We implement our method using a graph neural network as the constraint function and gradient descent as the constraint solver. Our model achieves better or comparable performance to top learned simulators.
  arXiv  Detail & Related papers  (2021-12-16T19:15:11Z)
• SimNet: Computer Architecture Simulation using Machine Learning [3.7019798164954336]
  This work describes a concerted effort, where machine learning (ML) is used to accelerate discrete-event simulation. A GPU-accelerated parallel simulator is implemented based on the proposed instruction latency predictor. Its simulation accuracy and throughput are validated and evaluated against a state-of-the-art simulator.
  arXiv  Detail & Related papers  (2021-05-12T17:31:52Z)

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.