Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design

• URL: http://arxiv.org/abs/2302.00557v1
• Date: Wed, 1 Feb 2023 16:23:29 GMT
• Title: Graph Neural Network Based Surrogate Model of Physics Simulations for Geometry Design
• Authors: Jian Cheng Wong, Chin Chun Ooi, Joyjit Chattoraj, Lucas Lestandi, Guoying Dong, Umesh Kizhakkinan, David William Rosen, Mark Hyunpong Jhon, My Ha Dao
• Abstract summary: We develop graph neural networks (GNNs) as fast surrogate models for physics simulation. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications.
• Score: 0.20315704654772412
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Computational Intelligence (CI) techniques have shown great potential as a surrogate model of expensive physics simulation, with demonstrated ability to make fast predictions, albeit at the expense of accuracy in some cases. For many scientific and engineering problems involving geometrical design, it is desirable for the surrogate models to precisely describe the change in geometry and predict the consequences. In that context, we develop graph neural networks (GNNs) as fast surrogate models for physics simulation, which allow us to directly train the models on 2/3D geometry designs that are represented by an unstructured mesh or point cloud, without the need for any explicit or hand-crafted parameterization. We utilize an encoder-processor-decoder-type architecture which can flexibly make prediction at both node level and graph level. The performance of our proposed GNN-based surrogate model is demonstrated on 2 example applications: feature designs in the domain of additive engineering and airfoil design in the domain of aerodynamics. The models show good accuracy in their predictions on a separate set of test geometries after training, with almost instant prediction speeds, as compared to O(hour) for the high-fidelity simulations required otherwise.

Related papers

• Generative Aerodynamic Design with Diffusion Probabilistic Models [0.7373617024876725]
  We show that generative models have the potential to provide geometries by generalizing geometries over a large dataset of simulations. In particular, we leverage diffusion probabilistic models trained on XFOIL simulations to synthesize two-dimensional airfoil geometries conditioned on given aerodynamic features and constraints. We show that the models are able to generate diverse candidate designs for identical requirements and constraints, effectively exploring the design space to provide multiple starting points to optimization procedures.
  arXiv  Detail & Related papers  (2024-09-20T08:38:36Z)
• Physics-Informed Geometric Operators to Support Surrogate, Dimension Reduction and Generative Models for Engineering Design [38.00713966087315]
  We propose a set of physics-informed geometric operators (GOs) to enrich the geometric data provided for training surrogate/discriminative models. GOs exploit the differential and integral properties of shapes to infuse high-level intrinsic geometric information and physics into the feature vector used for training.
  arXiv  Detail & Related papers  (2024-07-10T12:50:43Z)
• Bayesian Mesh Optimization for Graph Neural Networks to Enhance Engineering Performance Prediction [1.6574413179773761]
  In engineering design, surrogate models are widely employed to replace computationally expensive simulations. We propose a Bayesian graph neural network (GNN) framework for a 3D deep-learning-based surrogate model. Our framework determines the optimal size of mesh elements through Bayesian optimization, resulting in a high-accuracy surrogate model.
  arXiv  Detail & Related papers  (2024-06-04T06:27:48Z)
• Multi-GPU Approach for Training of Graph ML Models on large CFD Meshes [0.0]
  Mesh-based numerical solvers are an important part in many design tool chains. Machine Learning based surrogate models are fast in predicting approximate solutions but often lack accuracy. This paper scales a state-of-the-art surrogate model from the domain of graph-based machine learning to industry-relevant mesh sizes.
  arXiv  Detail & Related papers  (2023-07-25T15:49:25Z)
• Automatic Parameterization for Aerodynamic Shape Optimization via Deep Geometric Learning [60.69217130006758]
  We propose two deep learning models that fully automate shape parameterization for aerodynamic shape optimization. Both models are optimized to parameterize via deep geometric learning to embed human prior knowledge into learned geometric patterns. We perform shape optimization experiments on 2D airfoils and discuss the applicable scenarios for the two models.
  arXiv  Detail & Related papers  (2023-05-03T13:45:40Z)
• FAENet: Frame Averaging Equivariant GNN for Materials Modeling [123.19473575281357]
  We introduce a flexible framework relying on frameaveraging (SFA) to make any model E(3)-equivariant or invariant through data transformations. We prove the validity of our method theoretically and empirically demonstrate its superior accuracy and computational scalability in materials modeling.
  arXiv  Detail & Related papers  (2023-04-28T21:48:31Z)
• Learning Large-scale Subsurface Simulations with a Hybrid Graph Network Simulator [57.57321628587564]
  We introduce Hybrid Graph Network Simulator (HGNS) for learning reservoir simulations of 3D subsurface fluid flows. HGNS consists of a subsurface graph neural network (SGNN) to model the evolution of fluid flows, and a 3D-U-Net to model the evolution of pressure. Using an industry-standard subsurface flow dataset (SPE-10) with 1.1 million cells, we demonstrate that HGNS is able to reduce the inference time up to 18 times compared to standard subsurface simulators.
  arXiv  Detail & Related papers  (2022-06-15T17:29:57Z)
• A physics and data co-driven surrogate modeling approach for temperature field prediction on irregular geometric domain [12.264200001067797]
  We propose a novel physics and data co-driven surrogate modeling method for temperature field prediction. Numerical results demonstrate that our method can significantly improve accuracy prediction on a smaller dataset.
  arXiv  Detail & Related papers  (2022-03-15T08:43:24Z)
• ForceNet: A Graph Neural Network for Large-Scale Quantum Calculations [86.41674945012369]
  We develop a scalable and expressive Graph Neural Networks model, ForceNet, to approximate atomic forces. Our proposed ForceNet is able to predict atomic forces more accurately than state-of-the-art physics-based GNNs.
  arXiv  Detail & Related papers  (2021-03-02T03:09:06Z)
• 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)
• Machine learning for rapid discovery of laminar flow channel wall modifications that enhance heat transfer [56.34005280792013]
  We present a combination of accurate numerical simulations of arbitrary, flat, and non-flat channels and machine learning models predicting drag coefficient and Stanton number. We show that convolutional neural networks (CNN) can accurately predict the target properties at a fraction of the time of numerical simulations.
  arXiv  Detail & Related papers  (2021-01-19T16:14:02Z)

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.