Simulating Liquids with Graph Networks

• URL: http://arxiv.org/abs/2203.07895v1
• Date: Mon, 14 Mar 2022 15:39:27 GMT
• Title: Simulating Liquids with Graph Networks
• Authors: Jonathan Klimesch, Philipp Holl, Nils Thuerey
• Abstract summary: We investigate graph neural networks (GNNs) for learning fluid dynamics. Our results indicate that learning models, such as GNNs, fail to learn the exact underlying dynamics unless the training set is devoid of any other problem-specific correlations.
• Score: 25.013244956897832
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulating complex dynamics like fluids with traditional simulators is computationally challenging. Deep learning models have been proposed as an efficient alternative, extending or replacing parts of traditional simulators. We investigate graph neural networks (GNNs) for learning fluid dynamics and find that their generalization capability is more limited than previous works would suggest. We also challenge the current practice of adding random noise to the network inputs in order to improve its generalization capability and simulation stability. We find that inserting the real data distribution, e.g. by unrolling multiple simulation steps, improves accuracy and that hiding all domain-specific features from the learning model improves generalization. Our results indicate that learning models, such as GNNs, fail to learn the exact underlying dynamics unless the training set is devoid of any other problem-specific correlations that could be used as shortcuts.

Related papers

• FMint: Bridging Human Designed and Data Pretrained Models for Differential Equation Foundation Model [5.748690310135373]
  We propose a novel multi-modal foundation model, named textbfFMint, to bridge the gap between human-designed and data-driven models. Built on a decoder-only transformer architecture with in-context learning, FMint utilizes both numerical and textual data to learn a universal error correction scheme. Our results demonstrate the effectiveness of the proposed model in terms of both accuracy and efficiency compared to classical numerical solvers.
  arXiv  Detail & Related papers  (2024-04-23T02:36:47Z)
• 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)
• Three-dimensional granular flow simulation using graph neural network-based learned simulator [2.153852088624324]
  We use a graph neural network (GNN) to develop a simulator for granular flows. The simulator reproduces the overall behaviors of column collapses with various aspect ratios. The speed of GNS outperforms high-fidelity numerical simulators by 300 times.
  arXiv  Detail & Related papers  (2023-11-13T15:54:09Z)
• Latent Task-Specific Graph Network Simulators [16.881339139068018]
  Graph Network Simulators (GNSs) pose an efficient alternative to traditional physics-based simulators. We frame mesh-based simulation as a meta-learning problem and use a recent Bayesian meta-learning method to improve GNSs adaptability to new scenarios. We validate the effectiveness of our approach through various experiments, performing on par with or better than established baseline methods.
  arXiv  Detail & Related papers  (2023-11-09T10:30:51Z)
• How neural networks learn to classify chaotic time series [77.34726150561087]
  We study the inner workings of neural networks trained to classify regular-versus-chaotic time series. We find that the relation between input periodicity and activation periodicity is key for the performance of LKCNN models.
  arXiv  Detail & Related papers  (2023-06-04T08:53:27Z)
• 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)
• Continual learning autoencoder training for a particle-in-cell simulation via streaming [52.77024349608834]
  upcoming exascale era will provide a new generation of physics simulations with high resolution. These simulations will have a high resolution, which will impact the training of machine learning models since storing a high amount of simulation data on disk is nearly impossible. This work presents an approach that trains a neural network concurrently to a running simulation without data on a disk.
  arXiv  Detail & Related papers  (2022-11-09T09:55:14Z)
• Real-to-Sim: Predicting Residual Errors of Robotic Systems with Sparse Data using a Learning-based Unscented Kalman Filter [65.93205328894608]
  We learn the residual errors between a dynamic and/or simulator model and the real robot. We show that with the learned residual errors, we can further close the reality gap between dynamic models, simulations, and actual hardware.
  arXiv  Detail & Related papers  (2022-09-07T15:15:12Z)
• Pretraining Graph Neural Networks for few-shot Analog Circuit Modeling and Design [68.1682448368636]
  We present a supervised pretraining approach to learn circuit representations that can be adapted to new unseen topologies or unseen prediction tasks. To cope with the variable topological structure of different circuits we describe each circuit as a graph and use graph neural networks (GNNs) to learn node embeddings. We show that pretraining GNNs on prediction of output node voltages can encourage learning representations that can be adapted to new unseen topologies or prediction of new circuit level properties.
  arXiv  Detail & Related papers  (2022-03-29T21:18:47Z)
• Constraint-based graph network simulator [9.462808515258464]
  We present a framework for constraint-based learned simulation. We implement our method using a graph neural network as the constraint function and gradient descent as the constraint solver. Our model achieves better or comparable performance to top learned simulators.
  arXiv  Detail & Related papers  (2021-12-16T19:15:11Z)
• Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction [79.81193813215872]
  We develop a hybrid (graph) neural network that combines a traditional graph convolutional network with an embedded differentiable fluid dynamics simulator inside the network itself. We show that we can both generalize well to new situations and benefit from the substantial speedup of neural network CFD predictions.
  arXiv  Detail & Related papers  (2020-07-08T21:23:19Z)

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.