DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations

• URL: http://arxiv.org/abs/2208.08956v1
• Date: Thu, 18 Aug 2022 16:59:27 GMT
• Title: DeepClouds.ai: Deep learning enabled computationally cheap direct numerical simulations
• Authors: Moumita Bhowmik, Manmeet Singh, Suryachandra Rao, Souvik Paul
• Abstract summary: We introduce DeepClouds.ai, a 3D-UNET that simulates the outputs of a rising cloud DNS experiment. This framework can be used to further the science of turbulence and cloud flows by enabling simulations over large physical domains in the atmosphere.
• Score: 1.3649494534428745
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Simulation of turbulent flows, especially at the edges of clouds in the atmosphere, is an inherently challenging task. Hitherto, the best possible computational method to perform such experiments is the Direct Numerical Simulation (DNS). DNS involves solving non-linear partial differential equations for fluid flows, also known as Navier-Stokes equations, on discretized grid boxes in a three-dimensional space. It is a valuable paradigm that has guided the numerical weather prediction models to compute rainfall formation. However, DNS cannot be performed for large domains of practical utility to the weather forecast community. Here, we introduce DeepClouds.ai, a 3D-UNET that simulates the outputs of a rising cloud DNS experiment. The problem of increasing the domain size in DNS is addressed by mapping an inner 3D cube to the complete 3D cube from the output of the DNS discretized grid simulation. Our approach effectively captures turbulent flow dynamics without having to solve the complex dynamical core. The baseline shows that the deep learning-based simulation is comparable to the partial-differential equation-based model as measured by various score metrics. This framework can be used to further the science of turbulence and cloud flows by enabling simulations over large physical domains in the atmosphere. It would lead to cascading societal benefits by improved weather predictions via advanced parameterization schemes.

Related papers

• Physics-enhanced Neural Operator for Simulating Turbulent Transport [9.923888452768919]
  This paper presents a physics-enhanced neural operator (PENO) that incorporates physical knowledge of partial differential equations (PDEs) to accurately model flow dynamics. The proposed method is evaluated through its performance on two distinct sets of 3D turbulent flow data.
  arXiv  Detail & Related papers  (2024-05-31T20:05:17Z)
• 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)
• Geometry-Informed Neural Operator for Large-Scale 3D PDEs [76.06115572844882]
  We propose the geometry-informed neural operator (GINO) to learn the solution operator of large-scale partial differential equations. We successfully trained GINO to predict the pressure on car surfaces using only five hundred data points.
  arXiv  Detail & Related papers  (2023-09-01T16:59:21Z)
• Dynamic Deep Learning LES Closures: Online Optimization With Embedded DNS [0.0]
  We develop a new online training method for deep learning closure models in large-eddy simulation (LES) Deep learning closure model is dynamically trained during LES calculation using embedded direct numerical simulation (DNS) data. An online optimization algorithm is developed to dynamically train the deep learning closure model in the coupled, LES-embedded DNS calculation.
  arXiv  Detail & Related papers  (2023-03-04T06:20:47Z)
• 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)
• 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)
• Reconstructing High-resolution Turbulent Flows Using Physics-Guided Neural Networks [3.9548535445908928]
  Direct numerical simulation (DNS) of turbulent flows is computationally expensive and cannot be applied to flows with large Reynolds numbers. Large eddy simulation (LES) is an alternative that is computationally less demanding, but is unable to capture all of the scales of turbulent transport accurately. We build a new data-driven methodology based on super-resolution techniques to reconstruct DNS data from LES predictions.
  arXiv  Detail & Related papers  (2021-09-06T03:01:24Z)
• Designing Air Flow with Surrogate-assisted Phenotypic Niching [117.44028458220427]
  We introduce surrogate-assisted phenotypic niching, a quality diversity algorithm. It allows to discover a large, diverse set of behaviors by using computationally expensive phenotypic features. In this work we discover the types of air flow in a 2D fluid dynamics optimization problem.
  arXiv  Detail & Related papers  (2021-05-10T10:45:28Z)
• Machine learning accelerated computational fluid dynamics [9.077691121640333]
  We use end-to-end deep learning to improve approximations inside computational fluid dynamics for modeling two-dimensional turbulent flows. For both direct numerical simulation of turbulence and large eddy simulation, our results are as accurate as baseline solvers with 8-10x finer resolution in each spatial dimension. Our approach exemplifies how scientific computing can leverage machine learning and hardware accelerators to improve simulations without sacrificing accuracy or generalization.
  arXiv  Detail & Related papers  (2021-01-28T19:10:00Z)
• 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)
• 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.