ElastoGen: 4D Generative Elastodynamics

• URL: http://arxiv.org/abs/2405.15056v2
• Date: Wed, 02 Oct 2024 01:49:56 GMT
• Title: ElastoGen: 4D Generative Elastodynamics
• Authors: Yutao Feng, Yintong Shang, Xiang Feng, Lei Lan, Shandian Zhe, Tianjia Shao, Hongzhi Wu, Kun Zhou, Hao Su, Chenfanfu Jiang, Yin Yang,
• Abstract summary: ElastoGen is a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Because of its alignment with actual physical procedures, ElastoGen efficiently generates accurate dynamics for a wide range of hyperelastic materials.
• Score: 59.20029207991106
• License:
• Abstract: We present ElastoGen, a knowledge-driven AI model that generates physically accurate 4D elastodynamics. Unlike deep models that learn from video- or image-based observations, ElastoGen leverages the principles of physics and learns from established mathematical and optimization procedures. The core idea of ElastoGen is converting the differential equation, corresponding to the nonlinear force equilibrium, into a series of iterative local convolution-like operations, which naturally fit deep architectures. We carefully build our network module following this overarching design philosophy. ElastoGen is much more lightweight in terms of both training requirements and network scale than deep generative models. Because of its alignment with actual physical procedures, ElastoGen efficiently generates accurate dynamics for a wide range of hyperelastic materials and can be easily integrated with upstream and downstream deep modules to enable end-to-end 4D generation.

Related papers

• 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)
• Data-driven low-dimensional model of a sedimenting flexible fiber [0.0]
  This work describes a data-driven technique to create high-fidelity low-dimensional models of flexible fiber dynamics using machine learning. The approach combines an autoencoder neural network architecture to learn a low-dimensional latent representation of the filament shape. We show that our data-driven model can accurately forecast the evolution of a fiber at both trained and untrained elasto-gravitational numbers.
  arXiv  Detail & Related papers  (2024-05-16T21:07:09Z)
• DiffuseBot: Breeding Soft Robots With Physics-Augmented Generative Diffusion Models [102.13968267347553]
  We present DiffuseBot, a physics-augmented diffusion model that generates soft robot morphologies capable of excelling in a wide spectrum of tasks. We showcase a range of simulated and fabricated robots along with their capabilities.
  arXiv  Detail & Related papers  (2023-11-28T18:58:48Z)
• PIE-NeRF: Physics-based Interactive Elastodynamics with NeRF [29.6350855891474]
  We show that physics-based simulations can be seamlessly integrated with NeRF to generate high-quality elastodynamics of real-world objects. A quadratic generalized moving least square (Q-GMLS) is employed to capture nonlinear dynamics and large deformation on the implicit model. We adaptively place the least-square kernels according to the NeRF density field to significantly reduce the complexity of the nonlinear simulation.
  arXiv  Detail & Related papers  (2023-11-22T01:58:26Z)
• SE(3)-Stochastic Flow Matching for Protein Backbone Generation [54.951832422425454]
  We introduce FoldFlow, a series of novel generative models of increasing modeling power based on the flow-matching paradigm over $3mathrmD$ rigid motions. Our family of FoldFlowgenerative models offers several advantages over previous approaches to the generative modeling of proteins.
  arXiv  Detail & Related papers  (2023-10-03T19:24:24Z)
• A physics-informed deep neural network for surrogate modeling in classical elasto-plasticity [0.0]
  We present a deep neural network architecture that can efficiently approximate classical elasto-plastic relations. The network is enriched with crucial physics aspects of classical elasto-plasticity, including additive decomposition of strains into elastic and plastic parts. We show that embedding these physics into the architecture of the neural network facilitates a more efficient training of the network with less training data.
  arXiv  Detail & Related papers  (2022-04-26T05:58:13Z)
• 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)
• Sobolev training of thermodynamic-informed neural networks for smoothed elasto-plasticity models with level set hardening [0.0]
  We introduce a deep learning framework designed to train smoothed elastoplasticity models with interpretable components. By recasting the yield function as an evolving level set, we introduce a machine learning approach to predict the solutions of the Hamilton-Jacobi equation.
  arXiv  Detail & Related papers  (2020-10-15T22:43:32Z)
• Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference [22.267489467486467]
  This paper proposes Nimble, a high-performance and flexible system to optimize, compile, and execute dynamic neural networks on multiple platforms. Our evaluation demonstrates that Nimble outperforms state-of-the-art deep learning frameworks and runtime systems for dynamic neural networks by up to 20x on hardware platforms.
  arXiv  Detail & Related papers  (2020-06-04T17:39:58Z)
• Learning Stable Deep Dynamics Models [91.90131512825504]
  We propose an approach for learning dynamical systems that are guaranteed to be stable over the entire state space. We show that such learning systems are able to model simple dynamical systems and can be combined with additional deep generative models to learn complex dynamics.
  arXiv  Detail & Related papers  (2020-01-17T00:04:45Z)

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.