Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow

• URL: http://arxiv.org/abs/2111.00328v1
• Date: Sat, 30 Oct 2021 20:41:28 GMT
• Title: Multi-Task Learning based Convolutional Models with Curriculum Learning for the Anisotropic Reynolds Stress Tensor in Turbulent Duct Flow
• Authors: Haitz S\'aez de Oc\'ariz Borde, David Sondak, Pavlos Protopapas
• Abstract summary: We propose a fully convolutional neural network that is able to accurately predict the normalized anisotropic Reynolds stress tensor for turbulent duct flow. We also explore the application of curriculum learning to data-driven turbulence modeling.
• Score: 1.6371837018687636
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The Reynolds-averaged Navier-Stokes (RANS) equations require accurate modeling of the anisotropic Reynolds stress tensor, for which traditional closure models only give good results in certain flow configurations. Researchers have started using machine learning approaches to address this problem. In this work we build upon recent convolutional neural network architectures used for turbulence modeling and propose a multi-task learning based fully convolutional neural network that is able to accurately predict the normalized anisotropic Reynolds stress tensor for turbulent duct flow. Furthermore, we also explore the application of curriculum learning to data-driven turbulence modeling.

Related papers

• Symmetry aware Reynolds Averaged Navier Stokes turbulence models with equivariant neural networks [0.6626421278252587]
  We introduce tensor-based, symmetry aware closures using equivariant neural networks (ENNs)<n>We present an algorithm for enforcing algebraic contraction relations among tensor components.
  arXiv  Detail & Related papers  (2025-11-12T22:04:05Z)
• Simulating Three-dimensional Turbulence with Physics-informed Neural Networks [5.7590724453740965]
  Turbulent fluid flows are among the most computationally demanding problems in science.<n>We show that appropriately designed PINNs can successfully simulate fully turbulent flows in both two and three dimensions.<n>Our results demonstrate that neural equation solvers can handle complex chaotic systems.
  arXiv  Detail & Related papers  (2025-07-11T19:02:52Z)
• Trajectory Flow Matching with Applications to Clinical Time Series Modeling [77.58277281319253]
  Trajectory Flow Matching (TFM) trains a Neural SDE in a simulation-free manner, bypassing backpropagation through the dynamics. We demonstrate improved performance on three clinical time series datasets in terms of absolute performance and uncertainty prediction.
  arXiv  Detail & Related papers  (2024-10-28T15:54:50Z)
• Epistemic Modeling Uncertainty of Rapid Neural Network Ensembles for Adaptive Learning [0.0]
  A new type of neural network is presented using the rapid neural network paradigm. It is found that the proposed emulator embedded neural network trains near-instantaneously, typically without loss of prediction accuracy.
  arXiv  Detail & Related papers  (2023-09-12T22:34:34Z)
• 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)
• Simple initialization and parametrization of sinusoidal networks via their kernel bandwidth [92.25666446274188]
  sinusoidal neural networks with activations have been proposed as an alternative to networks with traditional activation functions. We first propose a simplified version of such sinusoidal neural networks, which allows both for easier practical implementation and simpler theoretical analysis. We then analyze the behavior of these networks from the neural tangent kernel perspective and demonstrate that their kernel approximates a low-pass filter with an adjustable bandwidth.
  arXiv  Detail & Related papers  (2022-11-26T07:41:48Z)
• An advanced spatio-temporal convolutional recurrent neural network for storm surge predictions [73.4962254843935]
  We study the capability of artificial neural network models to emulate storm surge based on the storm track/size/intensity history. This study presents a neural network model that can predict storm surge, informed by a database of synthetic storm simulations.
  arXiv  Detail & Related papers  (2022-04-18T23:42:18Z)
• 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)
• Sparse Flows: Pruning Continuous-depth Models [107.98191032466544]
  We show that pruning improves generalization for neural ODEs in generative modeling. We also show that pruning finds minimal and efficient neural ODE representations with up to 98% less parameters compared to the original network, without loss of accuracy.
  arXiv  Detail & Related papers  (2021-06-24T01:40:17Z)
• Embedded training of neural-network sub-grid-scale turbulence models [0.0]
  The weights of a deep neural network model are optimized in conjunction with the governing flow equations to provide a model for sub-grid-scale stresses. The training is by a gradient descent method, which uses the adjoint Navier-Stokes equations to provide the end-to-end sensitivities of the model weights to the velocity fields.
  arXiv  Detail & Related papers  (2021-05-03T17:28:39Z)
• 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)
• Multi-fidelity Generative Deep Learning Turbulent Flows [0.0]
  In computational fluid dynamics, there is an inevitable trade off between accuracy and computational cost. In this work, a novel multi-fidelity deep generative model is introduced for the surrogate modeling of high-fidelity turbulent flow fields. The resulting surrogate is able to generate physically accurate turbulent realizations at a computational cost magnitudes lower than that of a high-fidelity simulation.
  arXiv  Detail & Related papers  (2020-06-08T16:37:48Z)
• 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.