Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning

• URL: http://arxiv.org/abs/2210.16110v1
• Date: Fri, 28 Oct 2022 13:14:06 GMT
• Title: Towards prediction of turbulent flows at high Reynolds numbers using high performance computing data and deep learning
• Authors: Mathis Bode and Michael Gauding and Jens Henrik G\"obbert and Baohao Liao and Jenia Jitsev and Heinz Pitsch
• Abstract summary: Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. DNS turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied.
• Score: 0.39146761527401425
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, deep learning (DL) methods are evaluated in the context of turbulent flows. Various generative adversarial networks (GANs) are discussed with respect to their suitability for understanding and modeling turbulence. Wasserstein GANs (WGANs) are then chosen to generate small-scale turbulence. Highly resolved direct numerical simulation (DNS) turbulent data is used for training the WGANs and the effect of network parameters, such as learning rate and loss function, is studied. Qualitatively good agreement between DNS input data and generated turbulent structures is shown. A quantitative statistical assessment of the predicted turbulent fields is performed.

Related papers

• DFA-GNN: Forward Learning of Graph Neural Networks by Direct Feedback Alignment [57.62885438406724]
  Graph neural networks are recognized for their strong performance across various applications. BP has limitations that challenge its biological plausibility and affect the efficiency, scalability and parallelism of training neural networks for graph-based tasks. We propose DFA-GNN, a novel forward learning framework tailored for GNNs with a case study of semi-supervised learning.
  arXiv  Detail & Related papers  (2024-06-04T07:24:51Z)
• Towards Long-Term predictions of Turbulence using Neural Operators [68.8204255655161]
  It aims to develop reduced-order/surrogate models for turbulent flow simulations using Machine Learning. Different model structures are analyzed, with U-NET structures performing better than the standard FNO in accuracy and stability.
  arXiv  Detail & Related papers  (2023-07-25T14:09:53Z)
• Deep Learning to advance the Eigenspace Perturbation Method for Turbulence Model Uncertainty Quantification [0.0]
  We outline a machine learning approach to aid the use of the Eigenspace Perturbation Method to predict the uncertainty in the turbulence model prediction. We use a trained neural network to predict the discrepancy in the shape of the RANS predicted Reynolds stress ellipsoid.
  arXiv  Detail & Related papers  (2022-02-11T08:06:52Z)
• How Well Do Sparse Imagenet Models Transfer? [75.98123173154605]
  Transfer learning is a classic paradigm by which models pretrained on large "upstream" datasets are adapted to yield good results on "downstream" datasets. In this work, we perform an in-depth investigation of this phenomenon in the context of convolutional neural networks (CNNs) trained on the ImageNet dataset. We show that sparse models can match or even outperform the transfer performance of dense models, even at high sparsities.
  arXiv  Detail & Related papers  (2021-11-26T11:58:51Z)
• Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations [86.5302150777089]
  Generative Adversarial Imputation Nets (GANs) and GAN-based techniques have attracted attention as unsupervised machine learning methods. We name our proposed method as Con Conval Generative Adversarial Imputation Nets (Conv-GAIN)
  arXiv  Detail & Related papers  (2021-11-03T03:50:48Z)
• Estimating permeability of 3D micro-CT images by physics-informed CNNs based on DNS [1.6274397329511197]
  This paper presents a novel methodology for permeability prediction from micro-CT scans of geological rock samples. The training data set for CNNs dedicated to permeability prediction consists of permeability labels that are typically generated by classical lattice Boltzmann methods (LBM) We instead perform direct numerical simulation (DNS) by solving the stationary Stokes equation in an efficient and distributed-parallel manner.
  arXiv  Detail & Related papers  (2021-09-04T08:43:19Z)
• An Ode to an ODE [78.97367880223254]
  We present a new paradigm for Neural ODE algorithms, called ODEtoODE, where time-dependent parameters of the main flow evolve according to a matrix flow on the group O(d) This nested system of two flows provides stability and effectiveness of training and provably solves the gradient vanishing-explosion problem.
  arXiv  Detail & Related papers  (2020-06-19T22:05:19Z)
• Network Diffusions via Neural Mean-Field Dynamics [52.091487866968286]
  We propose a novel learning framework for inference and estimation problems of diffusion on networks. Our framework is derived from the Mori-Zwanzig formalism to obtain an exact evolution of the node infection probabilities. Our approach is versatile and robust to variations of the underlying diffusion network models.
  arXiv  Detail & Related papers  (2020-06-16T18:45:20Z)
• Deep Learning of Dynamic Subsurface Flow via Theory-guided Generative Adversarial Network [0.0]
  Theory-guided generative adversarial network (TgGAN) is proposed to solve dynamic partial differential equations (PDEs) TgGAN is proposed for dynamic subsurface flow with heterogeneous model parameters. Numerical results demonstrate that the TgGAN model is robust and reliable for deep learning of dynamic PDEs.
  arXiv  Detail & Related papers  (2020-06-02T02:53:26Z)
• Automating Turbulence Modeling by Multi-Agent Reinforcement Learning [4.784658158364452]
  We introduce multi-agent reinforcement learning as an automated discovery tool of turbulence models. We demonstrate the potential of this approach on Large Eddy Simulations of homogeneous and isotropic turbulence.
  arXiv  Detail & Related papers  (2020-05-18T18:45:09Z)

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.