
Machine learning accelerated computational fluid dynamics
Numerical simulation of fluids plays an essential role in modeling many ...
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SURFNet: Superresolution of Turbulent Flows with Transfer Learning using Small Datasets
Deep Learning (DL) algorithms are emerging as a key alternative to compu...
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Deep Learning for Efficient Reconstruction of HighResolution Turbulent DNS Data
Within the domain of Computational Fluid Dynamics, Direct Numerical Simu...
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Dynamic Upsampling of Smoke through Dictionarybased Learning
Simulating turbulent smoke flows is computationally intensive due to the...
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Machine Learning Changes the Rules for Flux Limiters
Learning to integrate nonlinear equations from highly resolved direct n...
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Machine Learning Product State Distributions from Initial Reactant States for a Reactive AtomDiatom Collision System
A machine learned (ML) model for predicting product state distributions ...
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Combining data assimilation and machine learning to infer unresolved scale parametrisation
In recent years, machine learning (ML) has been proposed to devise data...
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Using Machine Learning to Augment CoarseGrid Computational Fluid Dynamics Simulations
Simulation of turbulent flows at high Reynolds number is a computationally challenging task relevant to a large number of engineering and scientific applications in diverse fields such as climate science, aerodynamics, and combustion. Turbulent flows are typically modeled by the NavierStokes equations. Direct Numerical Simulation (DNS) of the NavierStokes equations with sufficient numerical resolution to capture all the relevant scales of the turbulent motions can be prohibitively expensive. Simulation at lowerresolution on a coarsegrid introduces significant errors. We introduce a machine learning (ML) technique based on a deep neural network architecture that corrects the numerical errors induced by a coarsegrid simulation of turbulent flows at highReynolds numbers, while simultaneously recovering an estimate of the highresolution fields. Our proposed simulation strategy is a hybrid MLPDE solver that is capable of obtaining a meaningful highresolution solution trajectory while solving the system PDE at a lower resolution. The approach has the potential to dramatically reduce the expense of turbulent flow simulations. As a proofofconcept, we demonstrate our MLPDE strategy on a twodimensional turbulent (Rayleigh Number Ra=10^9) RayleighBénard Convection (RBC) problem.
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