Neural Stress Fields for Reduced-order Elastoplasticity and Fracture

• URL: http://arxiv.org/abs/2310.17790v1
• Date: Thu, 26 Oct 2023 21:37:32 GMT
• Title: Neural Stress Fields for Reduced-order Elastoplasticity and Fracture
• Authors: Zeshun Zong, Xuan Li, Minchen Li, Maurizio M. Chiaramonte, Wojciech Matusik, Eitan Grinspun, Kevin Carlberg, Chenfanfu Jiang, Peter Yichen Chen
• Abstract summary: We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. Key innovation is training a low-dimensional manifold for the Kirchhoff stress field via an implicit neural representation. We demonstrate dimension reduction by up to 100,000X and time savings by up to 10X.
• Score: 43.538728312264524
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We propose a hybrid neural network and physics framework for reduced-order modeling of elastoplasticity and fracture. State-of-the-art scientific computing models like the Material Point Method (MPM) faithfully simulate large-deformation elastoplasticity and fracture mechanics. However, their long runtime and large memory consumption render them unsuitable for applications constrained by computation time and memory usage, e.g., virtual reality. To overcome these barriers, we propose a reduced-order framework. Our key innovation is training a low-dimensional manifold for the Kirchhoff stress field via an implicit neural representation. This low-dimensional neural stress field (NSF) enables efficient evaluations of stress values and, correspondingly, internal forces at arbitrary spatial locations. In addition, we also train neural deformation and affine fields to build low-dimensional manifolds for the deformation and affine momentum fields. These neural stress, deformation, and affine fields share the same low-dimensional latent space, which uniquely embeds the high-dimensional simulation state. After training, we run new simulations by evolving in this single latent space, which drastically reduces the computation time and memory consumption. Our general continuum-mechanics-based reduced-order framework is applicable to any phenomena governed by the elastodynamics equation. To showcase the versatility of our framework, we simulate a wide range of material behaviors, including elastica, sand, metal, non-Newtonian fluids, fracture, contact, and collision. We demonstrate dimension reduction by up to 100,000X and time savings by up to 10X.

Related papers

• Simplicits: Mesh-Free, Geometry-Agnostic, Elastic Simulation [18.45850302604534]
  We present a data-, mesh-, and grid-free solution for elastic simulation for any object in any geometric representation. For each object, we fit a small implicit neural network encoding varying weights that act as a reduced deformation basis. Our experiments demonstrate the versatility, accuracy, and speed of this approach on data including signed distance functions, point clouds, neural primitives, tomography scans, radiance fields, Gaussian splats, surface meshes, and volume meshes.
  arXiv  Detail & Related papers  (2024-06-09T18:57:23Z)
• NeuralClothSim: Neural Deformation Fields Meet the Thin Shell Theory [70.10550467873499]
  We propose NeuralClothSim, a new quasistatic cloth simulator using thin shells. Our memory-efficient solver operates on a new continuous coordinate-based surface representation called neural deformation fields.
  arXiv  Detail & Related papers  (2023-08-24T17:59:54Z)
• NeuralStagger: Accelerating Physics-constrained Neural PDE Solver with Spatial-temporal Decomposition [67.46012350241969]
  This paper proposes a general acceleration methodology called NeuralStagger. It decomposing the original learning tasks into several coarser-resolution subtasks. We demonstrate the successful application of NeuralStagger on 2D and 3D fluid dynamics simulations.
  arXiv  Detail & Related papers  (2023-02-20T19:36:52Z)
• Exploring Physical Latent Spaces for High-Resolution Flow Restoration [22.924868896246334]
  We explore training deep neural network models in conjunction with physics simulations via partial differential equations (PDEs) In contrast to previous work, this paper treats the degrees of freedom of the simulated space purely as tools to be used by the neural network.
  arXiv  Detail & Related papers  (2022-11-21T09:41:18Z)
• 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)
• NN-EUCLID: deep-learning hyperelasticity without stress data [0.0]
  We propose a new approach for unsupervised learning of hyperelastic laws with physics-consistent deep neural networks. In contrast to supervised learning, which assumes the stress-strain, the approach only uses realistically measurable full-elastic field displacement and global force availability data.
  arXiv  Detail & Related papers  (2022-05-04T13:54:54Z)
• Physics Informed RNN-DCT Networks for Time-Dependent Partial Differential Equations [62.81701992551728]
  We present a physics-informed framework for solving time-dependent partial differential equations. Our model utilizes discrete cosine transforms to encode spatial and recurrent neural networks. We show experimental results on the Taylor-Green vortex solution to the Navier-Stokes equations.
  arXiv  Detail & Related papers  (2022-02-24T20:46:52Z)
• Scalable Differentiable Physics for Learning and Control [99.4302215142673]
  Differentiable physics is a powerful approach to learning and control problems that involve physical objects and environments. We develop a scalable framework for differentiable physics that can support a large number of objects and their interactions.
  arXiv  Detail & Related papers  (2020-07-04T19:07:51Z)

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.