Neural Modular Physics for Elastic Simulation

• URL: http://arxiv.org/abs/2512.15083v1
• Date: Wed, 17 Dec 2025 05:02:03 GMT
• Title: Neural Modular Physics for Elastic Simulation
• Authors: Yifei Li, Haixu Wu, Zeyi Xu, Tuur Stuyck, Wojciech Matusik,
• Abstract summary: This paper presents Neural Modular Physics (NMP) for elastic simulation.<n>NMP combines the approximation capacity of neural networks with the physical reliability of traditional simulators.<n>With a specialized architecture and training strategy, our method transforms the numerical computation flow into a modular neural simulator.
• Score: 36.621915661902115
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Learning-based methods have made significant progress in physics simulation, typically approximating dynamics with a monolithic end-to-end optimized neural network. Although these models offer an effective way to simulation, they may lose essential features compared to traditional numerical simulators, such as physical interpretability and reliability. Drawing inspiration from classical simulators that operate in a modular fashion, this paper presents Neural Modular Physics (NMP) for elastic simulation, which combines the approximation capacity of neural networks with the physical reliability of traditional simulators. Beyond the previous monolithic learning paradigm, NMP enables direct supervision of intermediate quantities and physical constraints by decomposing elastic dynamics into physically meaningful neural modules connected through intermediate physical quantities. With a specialized architecture and training strategy, our method transforms the numerical computation flow into a modular neural simulator, achieving improved physical consistency and generalizability. Experimentally, NMP demonstrates superior generalization to unseen initial conditions and resolutions, stable long-horizon simulation, better preservation of physical properties compared to other neural simulators, and greater feasibility in scenarios with unknown underlying dynamics than traditional simulators.

Related papers

• PhysRVG: Physics-Aware Unified Reinforcement Learning for Video Generative Models [100.65199317765608]
  Physical principles are fundamental to realistic visual simulation, but remain a significant oversight in transformer-based video generation.<n>We introduce a physics-aware reinforcement learning paradigm for video generation models that enforces physical collision rules directly in high-dimensional spaces.<n>We extend this paradigm to a unified framework, termed Mimicry-Discovery Cycle (MDcycle), which allows substantial fine-tuning.
  arXiv  Detail & Related papers  (2026-01-16T08:40:10Z)
• NeuSpring: Neural Spring Fields for Reconstruction and Simulation of Deformable Objects from Videos [80.0365394106039]
  We present NeuSpring, a neural spring field for the reconstruction and simulation of deformable objects from videos.<n> Experiments on real-world datasets demonstrate that our NeuSping achieves superior reconstruction and simulation performance for current state modeling and future prediction.
  arXiv  Detail & Related papers  (2025-11-11T14:40:09Z)
• Physics-Informed Learning of Proprietary Inverter Models for Grid Dynamic Studies [0.5999777817331317]
  We develop a physics-informed neural ordinary differential equations-based framework to emulate the proprietary dynamics of the inverters.<n>The proposed method is validated using a grid-forming inverter (GFM) case study.
  arXiv  Detail & Related papers  (2025-07-21T05:48:31Z)
• 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)
• Neural Material Adaptor for Visual Grounding of Intrinsic Dynamics [48.99021224773799]
  We propose the Neural Material Adaptor (NeuMA), which integrates existing physical laws with learned corrections. We also propose Particle-GS, a particle-driven 3D Gaussian Splatting variant that bridges simulation and observed images.
  arXiv  Detail & Related papers  (2024-10-10T17:43:36Z)
• Conditional Generative Models for Simulation of EMG During Naturalistic Movements [45.698312905115955]
  We present a conditional generative neural network trained adversarially to generate motor unit activation potential waveforms. We demonstrate the ability of such a model to predictively interpolate between a much smaller number of numerical model's outputs with a high accuracy.
  arXiv  Detail & Related papers  (2022-11-03T14:49:02Z)
• Learning to Simulate Unseen Physical Systems with Graph Neural Networks [13.202870928432045]
  "Graph-based Physics Engine" is a machine learning method embedded with physical priors and material parameters. We demonstrate that GPE can generalize to materials with different properties not seen in the training set. In addition, introducing the law of momentum conservation in the model significantly improves the efficiency and stability of learning.
  arXiv  Detail & Related papers  (2022-01-28T07:56:46Z)
• Mapping and Validating a Point Neuron Model on Intel's Neuromorphic Hardware Loihi [77.34726150561087]
  We investigate the potential of Intel's fifth generation neuromorphic chip - Loihi' Loihi is based on the novel idea of Spiking Neural Networks (SNNs) emulating the neurons in the brain. We find that Loihi replicates classical simulations very efficiently and scales notably well in terms of both time and energy performance as the networks get larger.
  arXiv  Detail & Related papers  (2021-09-22T16:52:51Z)

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.