DRLinFluids -- An open-source python platform of coupling Deep Reinforcement Learning and OpenFOAM

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   Machine learning accelerated computational fluid dynamics

   Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva et al. · 2021 · Fluid Dynamics · AI summary available

   Abstract

   Numerical simulation of fluids plays an essential role in modeling many physical phenomena, such as weather, climate, aerodynamics and plasma physics. Fluids are well described by the Navier-Stokes equations, but solving these equations at scale remains daunting, limited by the computational cost of resolving the smallest spatiotemporal features. This leads to unfavorable trade-offs between accuracy and tractability. Here 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 8-1…

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   Deep-reinforcement-learning-based separation control in a two-dimensional airfoil

   Xavier Garcia, Arnau Miró, Pol Suárez et al. · 2025 · Fluid Dynamics · AI summary available

   Abstract

   The aim of this study is to discover new active-flow-control (AFC) techniques for separation mitigation in a two-dimensional NACA 0012 airfoil at a Reynolds number of 3000. To find these AFC strategies, a framework consisting of a deep-reinforcement-learning (DRL) agent has been used to determine the action strategies to apply to the flow. The actions involve blowing and suction through jets at the airfoil surface. The flow is simulated with the numerical code Alya, which is a low-dissipation finite-element code, on a high-performance computing system. Various control strategies obtained through DRL led to 43.9% drag reduction, while others yielded an increase in aerodynamic efficiency of 58…

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   NeurIPS 2024 ML4CFD Competition: Harnessing Machine Learning for Computational Fluid Dynamics in Airfoil Design

   Mouadh Yagoubi, David Danan, Milad Leyli-abadi et al. · 2024 · Fluid Dynamics · AI summary available

   Abstract

   The integration of machine learning (ML) techniques for addressing intricate physics problems is increasingly recognized as a promising avenue for expediting simulations. However, assessing ML-derived physical models poses a significant challenge for their adoption within industrial contexts. This competition is designed to promote the development of innovative ML approaches for tackling physical challenges, leveraging our recently introduced unified evaluation framework known as Learning Industrial Physical Simulations (LIPS). Building upon the preliminary edition held from November 2023 to March 2024, this iteration centers on a task fundamental to a well-established physical application: …

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   Fluid Intelligence: A Forward Look on AI Foundation Models in Computational Fluid Dynamics

   Neil Ashton, Johannes Brandstetter, Siddhartha Mishra · 2025 · Fluid Dynamics · AI summary available

   Abstract

   Driven by the advancement of GPUs and AI, the field of Computational Fluid Dynamics (CFD) is undergoing significant transformations. This paper bridges the gap between the machine learning and CFD communities by deconstructing industrial-scale CFD simulations into their core components. Our main contribution is to propose the first scaling law that incorporates CFD inputs for both data generation and model training to outline the unique challenges of developing and deploying these next-generation AI models for complex fluid dynamics problems. Using our new scaling law, we establish quantitative estimates for the large-scale limit, distinguishing between regimes where the cost of data generat…

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   Enhancing Computational Fluid Dynamics with Machine Learning

   Ricardo Vinuesa, Steven L. Brunton · 2021 · Fluid Dynamics · AI summary available

   Abstract

   Machine learning is rapidly becoming a core technology for scientific computing, with numerous opportunities to advance the field of computational fluid dynamics. In this Perspective, we highlight some of the areas of highest potential impact, including to accelerate direct numerical simulations, to improve turbulence closure modeling, and to develop enhanced reduced-order models. We also discuss emerging areas of machine learning that are promising for computational fluid dynamics, as well as some potential limitations that should be taken into account.

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   Physics-informed neural networks (PINNs) for fluid mechanics: A review

   Shengze Cai, Zhiping Mao, Zhicheng Wang et al. · 2021 · Fluid Dynamics · AI summary available

   Abstract

   Despite the significant progress over the last 50 years in simulating flow problems using numerical discretization of the Navier-Stokes equations (NSE), we still cannot incorporate seamlessly noisy data into existing algorithms, mesh-generation is complex, and we cannot tackle high-dimensional problems governed by parametrized NSE. Moreover, solving inverse flow problems is often prohibitively expensive and requires complex and expensive formulations and new computer codes. Here, we review flow physics-informed learning, integrating seamlessly data and mathematical models, and implementing them using physics-informed neural networks (PINNs). We demonstrate the effectiveness of PINNs for inve…

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   Wall Modeling of Turbulent Flows with Various Pressure Gradients Using Multi-Agent Reinforcement Learning

   Di Zhou, H. Jane Bae · 2023 · Fluid Dynamics · AI summary available

   Abstract

   We propose a framework for developing wall models for large-eddy simulation that is able to capture pressure-gradient effects using multi-agent reinforcement learning. Within this framework, the distributed reinforcement learning agents receive off-wall environmental states including pressure gradient and turbulence strain rate, ensuring adaptability to a wide range of flows characterized by pressure-gradient effects and separations. Based on these states, the agents determine an action to adjust the wall eddy viscosity, and consequently the wall-shear stress. The model training is in-situ with wall-modeled large-eddy simulation grid resolutions and does not rely on the instantaneous velocit…

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   Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D

   Nils Wandel, Michael Weinmann, Reinhard Klein · 2020 · Fluid Dynamics · Not summarized yet

   Abstract

   Physically plausible fluid simulations play an important role in modern computer graphics and engineering. However, in order to achieve real-time performance, computational speed needs to be traded-off with physical accuracy. Surrogate fluid models based on neural networks have the potential to achieve both, fast fluid simulations and high physical accuracy. However, these approaches rely on massive amounts of training data, require complex pipelines for training and inference or do not generalize to new fluid domains. In this work, we present significant extensions to a recently proposed deep learning framework, which addresses the aforementioned challenges in 2D. We go from 2D to 3D and pr…