MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics

• URL: http://arxiv.org/abs/2212.01428v1
• Date: Fri, 2 Dec 2022 20:22:15 GMT
• Title: MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics
• Authors: Cooper Lorsung, Amir Barati Farimani
• Abstract summary: MeshDQN is developed as a general purpose deep reinforcement learning framework to iteratively coarsen meshes. A graph neural network based deep Q network is used to select meshes for removal and solution is used to bypass expensive simulations. MeshDQN successfully improves meshes for two 2D airfoils.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD pipelines. Adaptive meshing techniques allow the mesh to be updated automatically to produce an accurate solution for the problem at hand. Existing classical techniques for adaptive meshing require either additional functionality out of solvers, many training simulations, or both. Current machine learning techniques often require substantial computational cost for training data generation, and are restricted in scope to the training data flow regime. MeshDQN is developed as a general purpose deep reinforcement learning framework to iteratively coarsen meshes while preserving target property calculation. A graph neural network based deep Q network is used to select mesh vertices for removal and solution interpolation is used to bypass expensive simulations at each step in the improvement process. MeshDQN requires a single simulation prior to mesh coarsening, while making no assumptions about flow regime, mesh type, or solver, only requiring the ability to modify meshes directly in a CFD pipeline. MeshDQN successfully improves meshes for two 2D airfoils.

Related papers

• Iterative Sizing Field Prediction for Adaptive Mesh Generation From Expert Demonstrations [49.173541207550485]
  Adaptive Meshing By Expert Reconstruction (AMBER) is an imitation learning problem. AMBER combines a graph neural network with an online data acquisition scheme to predict the projected sizing field of an expert mesh. We experimentally validate AMBER on 2D meshes and 3D meshes provided by a human expert, closely matching the provided demonstrations and outperforming a single-step CNN baseline.
  arXiv  Detail & Related papers  (2024-06-20T10:01:22Z)
• PointSAGE: Mesh-independent superresolution approach to fluid flow predictions [0.0]
  High-resolution CFD simulations offer valuable insights into fluid behavior and flow patterns. As resolution increases, computational data requirements and time increase proportionately. We propose PointSAGE, a mesh-independent network to learn the complex fluid flow and directly predict fine simulations.
  arXiv  Detail & Related papers  (2024-04-06T12:49:09Z)
• Conformal Predictions Enhanced Expert-guided Meshing with Graph Neural Networks [8.736819316856748]
  This paper presents a machine learning-based scheme that utilize Graph Neural Networks (GNN) and expert guidance to automatically generate CFD meshes for aircraft models. We introduce a new 3D segmentation algorithm that outperforms two state-of-the-art models, PointNet++ and PointMLP, for surface classification. We also present a novel approach to project predictions from 3D mesh segmentation models to CAD surfaces using the conformal predictions method.
  arXiv  Detail & Related papers  (2023-08-14T14:39:13Z)
• Differentiable Turbulence II [0.0]
  We develop a framework for integrating deep learning models into a generic finite element numerical scheme for solving the Navier-Stokes equations. We show that the learned closure can achieve accuracy comparable to traditional large eddy simulation on a finer grid that amounts to an equivalent speedup of 10x.
  arXiv  Detail & Related papers  (2023-07-25T14:27:49Z)
• In Situ Framework for Coupling Simulation and Machine Learning with Application to CFD [51.04126395480625]
  Recent years have seen many successful applications of machine learning (ML) to facilitate fluid dynamic computations. As simulations grow, generating new training datasets for traditional offline learning creates I/O and storage bottlenecks. This work offers a solution by simplifying this coupling and enabling in situ training and inference on heterogeneous clusters.
  arXiv  Detail & Related papers  (2023-06-22T14:07:54Z)
• Learning Controllable Adaptive Simulation for Multi-resolution Physics [86.8993558124143]
  We introduce Learning controllable Adaptive simulation for Multi-resolution Physics (LAMP) as the first full deep learning-based surrogate model. LAMP consists of a Graph Neural Network (GNN) for learning the forward evolution, and a GNN-based actor-critic for learning the policy of spatial refinement and coarsening. We demonstrate that our LAMP outperforms state-of-the-art deep learning surrogate models, and can adaptively trade-off computation to improve long-term prediction error.
  arXiv  Detail & Related papers  (2023-05-01T23:20:27Z)
• Machine Learning model for gas-liquid interface reconstruction in CFD numerical simulations [59.84561168501493]
  The volume of fluid (VoF) method is widely used in multi-phase flow simulations to track and locate the interface between two immiscible fluids. A major bottleneck of the VoF method is the interface reconstruction step due to its high computational cost and low accuracy on unstructured grids. We propose a machine learning enhanced VoF method based on Graph Neural Networks (GNN) to accelerate the interface reconstruction on general unstructured meshes.
  arXiv  Detail & Related papers  (2022-07-12T17:07:46Z)
• M2N: Mesh Movement Networks for PDE Solvers [17.35053721712421]
  We present the first learning-based end-to-end mesh movement framework for PDE solvers. Key requirements are alleviating mesh, boundary consistency, and generalization to mesh with different resolutions. We validate our methods on stationary and time-dependent, linear and non-linear equations.
  arXiv  Detail & Related papers  (2022-04-24T04:23:31Z)
• Machine Learning-Based Optimal Mesh Generation in Computational Fluid Dynamics [0.0]
  We propose a machine learning approach to identify optimal mesh densities. We generate optimized meshes using classical methodologies and propose to train a convolutional network predicting optimal mesh densities. Using a training set of 20,000 simulations we achieve accuracies of more than 98.7%.
  arXiv  Detail & Related papers  (2021-02-25T15:25:17Z)
• A Meta-Learning Approach to the Optimal Power Flow Problem Under Topology Reconfigurations [69.73803123972297]
  We propose a DNN-based OPF predictor that is trained using a meta-learning (MTL) approach. The developed OPF-predictor is validated through simulations using benchmark IEEE bus systems.
  arXiv  Detail & Related papers  (2020-12-21T17:39:51Z)
• 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.