Machine learning accelerated computational fluid dynamics

Ranked by how close their title and abstract are to this paper’s. Select some to read them together.

Closest papers

1. Very close Not relevant

   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.

2. Very close Not relevant

   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…

3. Close Not relevant

   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…

4. Close Not relevant

   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: …

5. Close Not relevant

   Fluid Simulation System Based on Graph Neural Network

   Qiang Liu, Wei Zhu, Xiyu Jia et al. · 2022 · Fluid Dynamics · Not summarized yet

   Abstract

   Traditional computational fluid dynamics calculates the physical information of the flow field by solving partial differential equations, which takes a long time to calculate and consumes a lot of computational resources. We build a fluid simulation simulator based on the graph neural network architecture. The simulator has fast computing speed and low consumption of computing resources. We regard the computational domain as a structural graph, and the computational nodes in the structural graph determine neighbor nodes through adaptive sampling. Building deep learning architectures with attention graph neural networks. The fluid simulation simulator is trained according to the simulation re…

6. Close Not relevant

   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…

7. Close Not relevant

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

   Qiulei Wang, Lei Yan, Gang Hu et al. · 2022 · Fluid Dynamics · AI summary available

   Abstract

   We propose an open-source python platform for applications of Deep Reinforcement Learning (DRL) in fluid mechanics. DRL has been widely used in optimizing decision-making in nonlinear and high-dimensional problems. Here, an agent maximizes a cumulative reward with learning a feedback policy by acting in an environment. In control theory terms, the cumulative reward would correspond to the cost function, the agent to the actuator, the environment to the measured signals and the learned policy to the feedback law. Thus, DRL assumes an interactive environment or, equivalently, control plant. The setup of a numerical simulation plant with DRL is challenging and time-consuming. In this work, a no…

8. Close Not relevant

   Data-driven and machine-learning based prediction of wave propagation behavior in dam-break flood

   Changli Li, Zheng Han, Yange Li et al. · 2022 · Fluid Dynamics · Not summarized yet

   Abstract

   The computational prediction of wave propagation in dam-break floods is a long-standing problem in hydrodynamics and hydrology. Until now, conventional numerical models based on Saint-Venant equations are the dominant approaches. Here we show that a machine learning model that is well-trained on a minimal amount of data, can help predict the long-term dynamic behavior of a one-dimensional dam-break flood with satisfactory accuracy. For this purpose, we solve the Saint-Venant equations for a one-dimensional dam-break flood scenario using the Lax-Wendroff numerical scheme and train the reservoir computing echo state network (RC-ESN) with the dataset by the simulation results consisting of time…