Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates

• URL: http://arxiv.org/abs/2006.07818v1
• Date: Sun, 14 Jun 2020 06:43:33 GMT
• Title: Alternating ConvLSTM: Learning Force Propagation with Alternate State Updates
• Authors: Congyue Deng, Tai-Jiang Mu, Shi-Min Hu
• Abstract summary: We introduce the alternating convolutional Long Short-Term Memory (Alt-ConvLSTM) that models the force propagation mechanisms in a deformable object with near-uniform material properties. We demonstrate how this novel scheme imitates the alternate updates of the first and second-order terms in the forward method of numerical PDE solvers. We validate our Alt-ConvLSTM on human soft tissue simulation with thousands of particles and consistent body pose changes.
• Score: 29.011464047344614
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Data-driven simulation is an important step-forward in computational physics when traditional numerical methods meet their limits. Learning-based simulators have been widely studied in past years; however, most previous works view simulation as a general spatial-temporal prediction problem and take little physical guidance in designing their neural network architectures. In this paper, we introduce the alternating convolutional Long Short-Term Memory (Alt-ConvLSTM) that models the force propagation mechanisms in a deformable object with near-uniform material properties. Specifically, we propose an accumulation state, and let the network update its cell state and the accumulation state alternately. We demonstrate how this novel scheme imitates the alternate updates of the first and second-order terms in the forward Euler method of numerical PDE solvers. Benefiting from this, our network only requires a small number of parameters, independent of the number of the simulated particles, and also retains the essential features in ConvLSTM, making it naturally applicable to sequential data with spatial inputs and outputs. We validate our Alt-ConvLSTM on human soft tissue simulation with thousands of particles and consistent body pose changes. Experimental results show that Alt-ConvLSTM efficiently models the material kinetic features and greatly outperforms vanilla ConvLSTM with only the single state update.

Related papers

• A Neural Material Point Method for Particle-based Simulations [5.4346288442609945]
  We present NeuralMPM, a neural emulation framework for particle-based simulations. NeuralMPM interpolates Lagrangian particles onto a fixed-size grid, computes updates on grid nodes using image-to-image neural networks, and interpolates back to the particles. We demonstrate the advantages of NeuralMPM on several datasets, including fluid dynamics and fluid-solid interactions.
  arXiv  Detail & Related papers  (2024-08-28T12:39:51Z)
• Bridging the Sim-to-Real Gap with Bayesian Inference [53.61496586090384]
  We present SIM-FSVGD for learning robot dynamics from data. We use low-fidelity physical priors to regularize the training of neural network models. We demonstrate the effectiveness of SIM-FSVGD in bridging the sim-to-real gap on a high-performance RC racecar system.
  arXiv  Detail & Related papers  (2024-03-25T11:29:32Z)
• A Multi-Grained Symmetric Differential Equation Model for Learning Protein-Ligand Binding Dynamics [74.93549765488103]
  In drug discovery, molecular dynamics simulation provides a powerful tool for predicting binding affinities, estimating transport properties, and exploring pocket sites. We propose NeuralMD, the first machine learning surrogate that can facilitate numerical MD and provide accurate simulations in protein-ligand binding. We show the efficiency and effectiveness of NeuralMD, with a 2000$times$ speedup over standard numerical MD simulation and outperforming all other ML approaches by up to 80% under the stability metric.
  arXiv  Detail & Related papers  (2024-01-26T09:35:17Z)
• Convolutional State Space Models for Long-Range Spatiotemporal Modeling [65.0993000439043]
  ConvS5 is an efficient variant for long-rangetemporal modeling. It significantly outperforms Transformers and ConvNISTTM on a long horizon Moving-Lab experiment while training 3X faster than ConvLSTM and generating samples 400X faster than Transformers.
  arXiv  Detail & Related papers  (2023-10-30T16:11:06Z)
• Real-time simulation of viscoelastic tissue behavior with physics-guided deep learning [0.8250374560598492]
  We propose a deep learning method for predicting displacement fields of soft tissues with viselastic properties. The proposed method achieves a better accuracy over the conventional CNN models. It is hoped that the present investigation will help in filling the gap in applying deep learning in virtual reality.
  arXiv  Detail & Related papers  (2023-01-11T18:17:10Z)
• Multi-fidelity Hierarchical Neural Processes [79.0284780825048]
  Multi-fidelity surrogate modeling reduces the computational cost by fusing different simulation outputs. We propose Multi-fidelity Hierarchical Neural Processes (MF-HNP), a unified neural latent variable model for multi-fidelity surrogate modeling. We evaluate MF-HNP on epidemiology and climate modeling tasks, achieving competitive performance in terms of accuracy and uncertainty estimation.
  arXiv  Detail & Related papers  (2022-06-10T04:54:13Z)
• Analytically Integratable Zero-restlength Springs for Capturing Dynamic Modes unrepresented by Quasistatic Neural Networks [6.601755525003559]
  We present a novel paradigm for modeling certain types of dynamic simulation in real-time with the aid of neural networks. We augment our quasistatic neural network (QNN) inference with a (real-time) dynamic simulation layer. We demonstrate that the spring parameters can be robustly learned from a surprisingly small amount of dynamic simulation data.
  arXiv  Detail & Related papers  (2022-01-25T06:44:15Z)
• A Note on Learning Rare Events in Molecular Dynamics using LSTM and Transformer [4.80427355202687]
  Recently successful examples on learning slow dynamics by LSTM are given with simulation data of low dimensional reaction coordinate. We show that the following three key factors significantly affect the performance of language model learning, namely dimensionality of reaction coordinates, temporal resolution and state partition.
  arXiv  Detail & Related papers  (2021-07-14T09:26:36Z)
• Deep Bayesian Active Learning for Accelerating Stochastic Simulation [74.58219903138301]
  Interactive Neural Process (INP) is a deep active learning framework for simulations and with active learning approaches. For active learning, we propose a novel acquisition function, Latent Information Gain (LIG), calculated in the latent space of NP based models. The results demonstrate STNP outperforms the baselines in the learning setting and LIG achieves the state-of-the-art for active learning.
  arXiv  Detail & Related papers  (2021-06-05T01:31:51Z)
• Recurrent convolutional neural network for the surrogate modeling of subsurface flow simulation [0.0]
  We propose to combine SegNet with ConvLSTM layers for the surrogate modeling of numerical flow simulation. Results show that the proposed method improves the performance of SegNet based surrogate model remarkably when the output of the simulation is time series data.
  arXiv  Detail & Related papers  (2020-10-08T09:34:48Z)
• 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.