Machine Learning for Fluid Mechanics

Abstract

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.

To appear in the Annual Reviews of Fluid Mechanics, 2020

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• Robust active flow control over a range of Reynolds numbers using an artificial neural network trained through deep reinforcement learning
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• Attention-Enhanced Neural Network Models for Turbulence Simulation
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• Machine learning flow control with few sensor feedback and measurement noise
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• DropTrack -- automatic droplet tracking using deep learning for microfluidic applications
• Deep Learning Enhanced Dynamic Mode Decomposition
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• Data-driven nonlinear aeroelastic models of morphing wings for control
• Artificial Neural Networks Modelling of Wall Pressure Spectra Beneath Turbulent Boundary Layers
• Assessments of epistemic uncertainty using Gaussian stochastic weight averaging for fluid-flow regression
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• Uncertainty-aware Surrogate Models for Airfoil Flow Simulations with Denoising Diffusion Probabilistic Models
• Deep Learning Closure Models for Large-Eddy Simulation of Flows around Bluff Bodies
• Single-snapshot machine learning for super-resolution of turbulence
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• $\text{FastSVD-ML-ROM}$: A Reduced-Order Modeling Framework based on Machine Learning for Real-Time Applications
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• A data-driven approach for the closure of RANS models by the divergence of the Reynolds Stress Tensor
• Data-driven Modeling of Rotating Detonation Waves
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• A deep learning approach for the computation of curvature in the level-set method
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• On the Dynamics of the Jet Wiping Process: Numerical Simulations and Modal Analysis
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• Physics-informed neural networks modeling for systems with moving immersed boundaries: application to an unsteady flow past a plunging foil
• Learning Closed-form Equations for Subgrid-scale Closures from High-fidelity Data: Promises and Challenges
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• A Digital Twin Framework for Liquid-cooled Supercomputers as Demonstrated at Exascale
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• Exploratory Lagrangian-Based Particle Tracing Using Deep Learning
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• Deep Learning of Conjugate Mappings
• Model-Based Reinforcement Learning for Control of Strongly-Disturbed Unsteady Aerodynamic Flows
• Neural Dynamical Operator: Continuous Spatial-Temporal Model with Gradient-Based and Derivative-Free Optimization Methods
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• Data-driven model order reduction for granular media
• Curriculum learning for data-driven modeling of dynamical systems
• A Machine Learning Approach to Classify Vortex Wakes of Energy Harvesting Oscillating Foils
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• Learning to traverse convective flows at moderate to high Rayleigh numbers
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