Neural Force Field: Learning Generalized Physical Representation from a Few Examples

• URL: http://arxiv.org/abs/2502.08987v2
• Date: Fri, 14 Feb 2025 06:29:09 GMT
• Title: Neural Force Field: Learning Generalized Physical Representation from a Few Examples
• Authors: Shiqian Li, Ruihong Shen, Chi Zhang, Yixin Zhu,
• Abstract summary: Current AI models, despite extensive training, still struggle to achieve similar generalization. We present Neural Force Field (NFF) a modeling framework built on Neural Ordinary Differential Equation (NODE) NFF captures fundamental physical concepts such as gravity, support, and collision in an interpretable manner.
• Score: 24.651024239605288
• License:
• Abstract: Physical reasoning is a remarkable human ability that enables rapid learning and generalization from limited experience. Current AI models, despite extensive training, still struggle to achieve similar generalization, especially in Out-of-distribution (OOD) settings. This limitation stems from their inability to abstract core physical principles from observations. A key challenge is developing representations that can efficiently learn and generalize physical dynamics from minimal data. Here we present Neural Force Field (NFF) a modeling framework built on Neural Ordinary Differential Equation (NODE) that learns interpretable force field representations which can be efficiently integrated through an Ordinary Differential Equation ( ODE) solver to predict object trajectories. Unlike existing approaches that rely on high-dimensional latent spaces, NFF captures fundamental physical concepts such as gravity, support, and collision in an interpretable manner. Experiments on two challenging physical reasoning tasks demonstrate that NFF, trained with only a few examples, achieves strong generalization to unseen scenarios. This physics-grounded representation enables efficient forward-backward planning and rapid adaptation through interactive refinement. Our work suggests that incorporating physics-inspired representations into learning systems can help bridge the gap between artificial and human physical reasoning capabilities.

Related papers

• Physics meets Topology: Physics-informed topological neural networks for learning rigid body dynamics [6.675805308519987]
  We introduce a novel framework for modeling rigid body dynamics and learning collision interactions. We propose a physics-informed message-passing neural architecture, embedding physical laws directly in the model. This work addresses the challenge of multi-entity dynamic interactions, with applications spanning diverse scientific and engineering domains.
  arXiv  Detail & Related papers  (2024-11-18T11:03:15Z)
• PIETRA: Physics-Informed Evidential Learning for Traversing Out-of-Distribution Terrain [35.21102019590834]
  Physics-Informed Evidential Traversability (PIETRA) is a self-supervised learning framework that integrates physics priors directly into the mathematical formulation of evidential neural networks. Our evidential network seamlessly transitions between learned and physics-based predictions for out-of-distribution inputs. PIETRA improves both learning accuracy and navigation performance in environments with significant distribution shifts.
  arXiv  Detail & Related papers  (2024-09-04T18:01:10Z)
• ContPhy: Continuum Physical Concept Learning and Reasoning from Videos [86.63174804149216]
  ContPhy is a novel benchmark for assessing machine physical commonsense. We evaluated a range of AI models and found that they still struggle to achieve satisfactory performance on ContPhy. We also introduce an oracle model (ContPRO) that marries the particle-based physical dynamic models with the recent large language models.
  arXiv  Detail & Related papers  (2024-02-09T01:09:21Z)
• PhysGraph: Physics-Based Integration Using Graph Neural Networks [9.016253794897874]
  We focus on the detail enhancement of coarse clothing geometry which has many applications including computer games, virtual reality and virtual try-on. Our contribution is based on a simple observation: evaluating forces is computationally relatively cheap for traditional simulation methods. We demonstrate that this idea leads to a learnable module that can be trained on basic internal forces of small mesh patches.
  arXiv  Detail & Related papers  (2023-01-27T16:47:10Z)
• Learning Physical Dynamics with Subequivariant Graph Neural Networks [99.41677381754678]
  Graph Neural Networks (GNNs) have become a prevailing tool for learning physical dynamics. Physical laws abide by symmetry, which is a vital inductive bias accounting for model generalization. Our model achieves on average over 3% enhancement in contact prediction accuracy across 8 scenarios on Physion and 2X lower rollout MSE on RigidFall.
  arXiv  Detail & Related papers  (2022-10-13T10:00:30Z)
• Neural Implicit Representations for Physical Parameter Inference from a Single Video [49.766574469284485]
  We propose to combine neural implicit representations for appearance modeling with neural ordinary differential equations (ODEs) for modelling physical phenomena. Our proposed model combines several unique advantages: (i) Contrary to existing approaches that require large training datasets, we are able to identify physical parameters from only a single video. The use of neural implicit representations enables the processing of high-resolution videos and the synthesis of photo-realistic images.
  arXiv  Detail & Related papers  (2022-04-29T11:55:35Z)
• Physics-informed ConvNet: Learning Physical Field from a Shallow Neural Network [0.180476943513092]
  Modelling and forecasting multi-physical systems remain a challenge due to unavoidable data scarcity and noise. New framework named physics-informed convolutional network (PICN) is recommended from a CNN perspective. PICN may become an alternative neural network solver in physics-informed machine learning.
  arXiv  Detail & Related papers  (2022-01-26T14:35:58Z)
• Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language [92.7638697243969]
  We propose a unified framework that can jointly learn visual concepts and infer physics models of objects from videos and language. This is achieved by seamlessly integrating three components: a visual perception module, a concept learner, and a differentiable physics engine.
  arXiv  Detail & Related papers  (2021-10-28T17:59:13Z)
• Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling [86.9726984929758]
  We focus on the integration of incomplete physics models into deep generative models. We propose a VAE architecture in which a part of the latent space is grounded by physics. We demonstrate generative performance improvements over a set of synthetic and real-world datasets.
  arXiv  Detail & Related papers  (2021-02-25T20:28:52Z)
• Visual Grounding of Learned Physical Models [66.04898704928517]
  Humans intuitively recognize objects' physical properties and predict their motion, even when the objects are engaged in complicated interactions. We present a neural model that simultaneously reasons about physics and makes future predictions based on visual and dynamics priors. Experiments show that our model can infer the physical properties within a few observations, which allows the model to quickly adapt to unseen scenarios and make accurate predictions into the future.
  arXiv  Detail & Related papers  (2020-04-28T17:06:38Z)

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.