Subspace Graph Physics: Real-Time Rigid Body-Driven Granular Flow Simulation

• URL: http://arxiv.org/abs/2111.10206v1
• Date: Thu, 18 Nov 2021 13:37:14 GMT
• Title: Subspace Graph Physics: Real-Time Rigid Body-Driven Granular Flow Simulation
• Authors: Amin Haeri and Krzysztof Skonieczny
• Abstract summary: This research advances machine learning methods for modeling rigid body-driven granular flows. In particular, this research considers the development of a subspace machine learning simulation approach.
• Score: 3.198144010381572
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: An important challenge in robotics is understanding the interactions between robots and deformable terrains that consist of granular material. Granular flows and their interactions with rigid bodies still pose several open questions. A promising direction for accurate, yet efficient, modeling is using continuum methods. Also, a new direction for real-time physics modeling is the use of deep learning. This research advances machine learning methods for modeling rigid body-driven granular flows, for application to terrestrial industrial machines as well as space robotics (where the effect of gravity is an important factor). In particular, this research considers the development of a subspace machine learning simulation approach. To generate training datasets, we utilize our high-fidelity continuum method, material point method (MPM). Principal component analysis (PCA) is used to reduce the dimensionality of data. We show that the first few principal components of our high-dimensional data keep almost the entire variance in data. A graph network simulator (GNS) is trained to learn the underlying subspace dynamics. The learned GNS is then able to predict particle positions and interaction forces with good accuracy. More importantly, PCA significantly enhances the time and memory efficiency of GNS in both training and rollout. This enables GNS to be trained using a single desktop GPU with moderate VRAM. This also makes the GNS real-time on large-scale 3D physics configurations (700x faster than our continuum method).

Related papers

• Physics-informed Ground Reaction Dynamics from Human Motion Capture [4.4795626402834055]
  We propose a novel method for estimating human ground reaction dynamics directly from motion capture data.<n>We introduce a highly accurate and robust method for computing ground reaction forces from motion capture data using Euler's integration scheme and PD algorithm.<n>The proposed approach was tested on the GroundLink dataset.
  arXiv  Detail & Related papers  (2025-07-02T04:02:16Z)
• Physics-Encoded Graph Neural Networks for Deformation Prediction under Contact [87.69278096528156]
  In robotics, it's crucial to understand object deformation during tactile interactions. We introduce a method using Physics-Encoded Graph Neural Networks (GNNs) for such predictions. We've made our code and dataset public to advance research in robotic simulation and grasping.
  arXiv  Detail & Related papers  (2024-02-05T19:21:52Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• Graph Neural Network-based surrogate model for granular flows [2.153852088624324]
  Granular flow dynamics is crucial for assessing various geotechnical risks, including landslides and debris flows. Traditional continuum and discrete numerical methods are limited by their computational cost in simulating large-scale systems. We develop a graph neural network-based simulator (GNS) that takes the current state of granular flow and predicts the next state using explicit integration by learning the local interaction laws.
  arXiv  Detail & Related papers  (2023-05-09T07:28:12Z)
• Hindsight States: Blending Sim and Real Task Elements for Efficient Reinforcement Learning [61.3506230781327]
  In robotics, one approach to generate training data builds on simulations based on dynamics models derived from first principles. Here, we leverage the imbalance in complexity of the dynamics to learn more sample-efficiently. We validate our method on several challenging simulated tasks and demonstrate that it improves learning both alone and when combined with an existing hindsight algorithm.
  arXiv  Detail & Related papers  (2023-03-03T21:55:04Z)
• Learning 3D Granular Flow Simulations [6.308272531414633]
  We present a Graph Neural Networks approach towards accurate modeling of complex 3D granular flow simulation processes created by the discrete element method LIGGGHTS. We discuss how to implement Graph Neural Networks that deal with 3D objects, boundary conditions, particle - particle, and particle - boundary interactions.
  arXiv  Detail & Related papers  (2021-05-04T17:27:59Z)
• PREPRINT: Comparison of deep learning and hand crafted features for mining simulation data [7.214140640112874]
  This paper addresses the task of extracting meaningful results in an automated manner from high dimensional data sets. We propose deep learning methods which are capable of processing such data and which can be trained to solve relevant tasks on simulation data. We compile a large dataset of 2D simulations of the flow field around airfoils which contains 16000 flow fields with which we tested and compared approaches.
  arXiv  Detail & Related papers  (2021-03-11T09:28:00Z)
• 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)
• Data-Efficient Learning for Complex and Real-Time Physical Problem Solving using Augmented Simulation [49.631034790080406]
  We present a task for navigating a marble to the center of a circular maze. We present a model that learns to move a marble in the complex environment within minutes of interacting with the real system.
  arXiv  Detail & Related papers  (2020-11-14T02:03:08Z)
• Scalable Graph Networks for Particle Simulations [1.933681537640272]
  We introduce an approach that transforms a fully-connected interaction graph into a hierarchical one. Using our approach, we are able to train models on much larger particle counts, even on a single GPU. Our approach retains high accuracy and efficiency even on large-scale gravitational N-body simulations.
  arXiv  Detail & Related papers  (2020-10-14T10:54:54Z)
• Fast Modeling and Understanding Fluid Dynamics Systems with Encoder-Decoder Networks [0.0]
  We show that an accurate deep-learning-based proxy model can be taught efficiently by a finite-volume-based simulator. Compared to traditional simulation, the proposed deep learning approach enables much faster forward computation. We quantify the sensitivity of the deep learning model to key physical parameters and hence demonstrate that the inversion problems can be solved with great acceleration.
  arXiv  Detail & Related papers  (2020-06-09T17:14:08Z)
• Learning to Simulate Complex Physics with Graph Networks [68.43901833812448]
  We present a machine learning framework and model implementation that can learn to simulate a wide variety of challenging physical domains. Our framework---which we term "Graph Network-based Simulators" (GNS)--represents the state of a physical system with particles, expressed as nodes in a graph, and computes dynamics via learned message-passing. Our results show that our model can generalize from single-timestep predictions with thousands of particles during training, to different initial conditions, thousands of timesteps, and at least an order of magnitude more particles at test time.
  arXiv  Detail & Related papers  (2020-02-21T16:44:28Z)

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.