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

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

   Nils Wandel, Michael Weinmann, Reinhard Klein · 2020 · Dynamique des fluides · Pas encore résumé

   Résumé scientifique

   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…

2. Proche Pas pertinent

   Machine learning accelerated computational fluid dynamics

   Dmitrii Kochkov, Jamie A. Smith, Ayya Alieva et al. · 2021 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   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…

3. Proche Pas pertinent

   Enhancing Computational Fluid Dynamics with Machine Learning

   Ricardo Vinuesa, Steven L. Brunton · 2021 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   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.

4. Proche Pas pertinent

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

   Neil Ashton, Johannes Brandstetter, Siddhartha Mishra · 2025 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   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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   Fluid Simulation System Based on Graph Neural Network

   Qiang Liu, Wei Zhu, Xiyu Jia et al. · 2022 · Dynamique des fluides · Pas encore résumé

   Résumé scientifique

   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. Proche Pas pertinent

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

   Mouadh Yagoubi, David Danan, Milad Leyli-abadi et al. · 2024 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   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: …

7. Apparenté Pas pertinent

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

   Qiulei Wang, Lei Yan, Gang Hu et al. · 2022 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   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. Apparenté Pas pertinent

   Simulating fluid-fluid displacement in a soft capillary tube: How compliance delays interfacial instability and bubble pinch-off

   Sthavishtha R. Bhopalam, Ruben Juanes, Hector Gomez · 2025 · Dynamique des fluides · Résumé IA disponible

   Résumé scientifique

   The displacement of a more viscous fluid by a less viscous immiscible fluid in confined geometries is a fundamental problem in multiphase flows. Recent experiments have shown that such fluid-fluid displacement in micro-capillary tubes can lead to interfacial instabilities and, eventually, bubble pinch-off. A critical yet often overlooked aspect of this system is the effect of the tube's deformability on the onset of interfacial instability and bubble pinch-off. Here, we present a computational fluid-structure interaction model and an algorithm to simulate this fluid-fluid displacement problem in a soft capillary tube. We use a phase-field model for the fluids and a nonlinear hyperelastic mod…