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Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey
| Date | Stars |
|---|---|
| 2026-07-31 | 361 |
| 2026-08-04 | 361 |
| 2026-08-06 | 361 |
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# Awesome-AI4CFD
[](https://awesome.re)
[](https://github.com/WillDreamer/Awesome-AI4CFD)
[](https://opensource.org/licenses/MIT)
<img src="./images/main.png" width="96%" height="96%">
## [Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey](https://arxiv.org/abs/2408.12171)
This review explores the recent advancements in enhancing Computational Fluid Dynamics (CFD) through Machine Learning (ML). The literature is systematically classified into three primary categories: Data-driven Surrogates, Physics-Informed Surrogates, and ML-assisted Numerical Solutions. Subsequently, we highlight applications of ML for CFD in critical scientific and engineering disciplines, including aerodynamics, atmospheric science, and biofluid dynamics, among others.
<font size=6><center><b> Awesome-AI4CFD </b> </center></font>
- [Awesome-AI4CFD](#awesome-ai4cfd)
- [Existing Benchmarks](#existing-benchmarks)
- [Data-driven Surrogates](#data-driven-surrogates)
- [Dependent on Discretization](#dependent-on-discretization)
- [On Structured Grids](#on-structured-grids)
- [On Unstructured Mesh](#on-unstructured-mesh)
- [On Lagrangian Particle](#on-lagrangian-particle)
- [Independent on Discretization](#independent-on-discretization)
- [Deep Operator Network](#deep-operator-network)
- [In Physical Space](#in-physical-space)
- [Fourier Neural Operator](#fourier-neural-operator)
- [Physics-driven Surrogates](#physics-driven-surrogates)
- [Physics-Informed Neural Network (PINN)](#physics-informed-neural-network-pinn)
- [Discretized PDE-Informed Neural Network](#discretized-pde-informed-neural-network)
- [ML-assisted Numerical Solutions](#ml-assisted-numerical-solutions)
- [Assist Simulation at Coarser Scales](#assist-simulation-at-coarser-scales)
- [Preconditioning](#preconditioning)
- [Miscellaneous](#miscellaneous)
- [Application Novelty](#application-novelty)
- [Aerodynamics](#aerodynamics)
- [Combustion \& Reacting Flow](#combustion--reacting-flow)
- [Atmosphere \& Ocean Science](#atmosphere--ocean-science)
- [Biology Fluid](#biology-fluid)
- [Plasma](#plasma)
- [Frontier Models](#frontier-models)
- [Foundation Models](#foundation-models)
- [Generative Models](#generative-models)
- [Contributing](#contributing)
---
## Existing Benchmarks
| Title | Venue | Date | Code | Note |
|:--------|:--------:|:--------:|:--------:|:--------:|
| <br> [**PDEBench**](https://arxiv.org/abs/2210.07182) <br> | NeurIPS 2022 | 2022-10-13 | [GitHub](https://github.com/pdebench/PDEBench) | Local Demo |
| <br> [**DeepXDE: A Deep Learning Library for Solving Differential Equations**](https://epubs.siam.org/doi/pdf/10.1137/19M1274067) <br> | SIAM Review | 2021-01 | [GitHub](https://github.com/lululxvi/deepxde) | - |
| <br> [**DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Graph-Based Drag Prediction**](https://arxiv.org/abs/2403.08055) <br> | arXiv | 2024-05 | [GitHub](https://github.com/Mohamedelrefaie/DrivAerNet) | - |
| <br> [**The Well: a Large-Scale Collection of Diverse Physics Simulations for Machine Learning**](https://arxiv.org/abs/2412.00568) <br> | NeurIPS 2024 DB Track | 2024-12 | [GitHub](https://github.com/PolymathicAI/the_well) | - |
---
## Data-driven Surrogates
### Dependent on Discretization
#### On Structured Grids
| Title | Venue | Date | Code | Demo Excerpt of 26,008 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:f14ba2ed4b600cba, llm:Repository title and description: 'Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey' (WillDreamer/Awesome-AI4CFD).
matched fp:f14ba2ed4b600cba, llm:Repository title and description: 'Recent Advances on Machine Learning for Computational Fluid Dynamics: A Survey' (WillDreamer/Awesome-AI4CFD).