Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
awesome-machine-learning-fluid-mechanics
Curated list for ML in FM
https://github.com/ikespand/awesome-machine-learning-fluid-mechanics
- TensorFlow - known machine learning library developed by Google.
- PyTorch
- Scikit-learn - purpose machine learning library. It also provides the implementation of several other data analysis algorithm.
- easyesn
- EchoTorch
- flowTorch
- neurodiffeq
- SciANN - informed deep learning.
- PySINDy
- smarties - performance C++ implementations of deep RL learning algorithms including V-RACER, CMA, PPO, DQN, DPG, ACER, and NAF.
- DRLinFluids - 1](https://doi.org/10.1063/5.0103113), [Paper-2](https://doi.org/10.1063/5.0152777)]
- PyDMD
- PYPARSVD
- turbESN - based package which relies on PyTorch for ESN as a backend which supports fully autonomous and teacher forced ESN predictions.
- PyKoopman - driven approximations to the Koopman operator. ([Paper](https://arxiv.org/abs/2306.12962))
- Editorial: Machine Learning and Physical Review Fluids: An Editorial Perspective
- An Old-Fashioned Framework for Machine Learning in Turbulence Modeling
- Paper
- arXiv
- Paper
- Paper
- arXiv
- arXiv
- Paper
- Paper
- arXiv - 022-00264-7))
- Paper
- arXiv
- Paper
- Open Access Paper
- Paper
- arXiv
- arXiv
- arXiv
- Paper
- Paper
- arXiv
- arXiv
- Open Access Paper
- Paper
- arXiv
- arXiv - Maulik/ML_2D_Turbulencehttp))
- Paper
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- Paper
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- Paper
- Paper
- Paper
- arXiv
- arXiv - Nek5000/DeepTurbulence "Code"))
- arXiv
- arXiv - Maulik/CAE_LSTM_ROMS))
- Paper - Maulik/ML_ROM_Closures))
- arXiv
- arXiv
- arXiv
- arXiv
- arXiv
- Paper - archives/pilotedjet/ch4-air/) | [Code](https://github.com/kamilazdybal/cost-function-manifold-assessment))
- arXiv - Fonzi/pysu2DMD))
- arXiv
- arXiv
- Paper
- arXiv
- arXiv - 024-45578-4) | [Code](https://github.com/KTH-FlowAI/beta-Variational-autoencoders-and-transformers-for-reduced-order-modelling-of-fluid-flows) | [Data](https://zenodo.org/records/10501216))
- Paper
- arXiv
- Paper
- arXiv
- arXiv - 024-54067-z) | [Code](https://github.com/dario-coscia/GAROM))
- Paper
- Paper
- arXiv
- arXiv
- Open Access Paper
- arXiv - 023-00657-y))
- arXiv - FlowAI/Identifying-regions-of-importance-in-wall-bounded-turbulence-through-explainable-deep-learning))
- Paper
- Paper
- ResearchGate - abstract/36/2/025120/3262840/Machine-learning-based-vorticity-evolution-and?redirectedFrom=fulltext))
- Paper
- arXiv
- arXiv - FlowAI/MARL-drag-reduction-in-wall-bounded-flows))
- arXiv - abstract/34/8/081801/2846652/DRLinFluids-An-open-source-Python-platform-of?redirectedFrom=fulltext) | [Code](https://github.com/venturi123/DRLinFluids))
- Paper
- Paper
- arXiv
- arXiv
- Paper
- Paper
- Paper
- Paper
- Paper
- arXiv - hae))
- arXiv - mg/2023_gmm_shock_sensor))
- International Workshop on Data-driven Modeling and Optimization in Fluid Mechanics
- Symposium on Model-Consistent Data-driven Turbulence Modeling
- Turbulence Modeling: Roadblocks, and the Potential for Machine Learning
- Mini symposia: Analysis of Real World and Industry Applications: emerging frontiers in CFD computing, machine learning and beyond
- IUTAM Symposium on Data-driven modeling and optimization in fluid mechanics
- 33rd Parallel Computational Fluid Dynamics International Conference
- Workshop: data-driven methods in fluid mechanics
- Lecture Series on Hands on Machine Learning for Fluid Dynamics 2023
- 629 – Data-driven fluid mechanics
- Machine Learning for Fluid Mechanics: Analysis, Modeling, Control and Closures
- Simulation data - database/experimental-data-1.791818 "Experimental data") | [Paper-1](https://www.cambridge.org/core/journals/journal-of-fluid-mechanics/article/history-effects-and-near-equilibrium-in-adversepressuregradient-turbulent-boundary-layers/39C38082C380F396D004B65F438C296A "Paper-1"))
- Data - 1](http://www.vremanresearch.nl/Vreman_Kuerten_Chan180_PF2014.pdf "Paper-1") | [Paper-2](http://www.vremanresearch.nl/Vreman_Kuerten_Chan590_PF2014.pdf "Paper-2"))
- Database
- Database
- Data - 1](https://www.sciencedirect.com/science/article/abs/pii/S0017931017353176 "Paper-1") | [Paper-2](https://www.sciencedirect.com/science/article/abs/pii/S0017931017307998 "Paper-2"))
- YouTube - 2013/lectures.html "Slides"))
- YouTube
- YouTube
- YouTube
- Machine learning in computational fluid dynamics
- this wonderful YouTube channel
- on this YouTube channel from Jousef Murad
- Data-Driven Fluid Mechanics: Combining First Principles and Machine Learning
- Convolutional Neural Networks for Steady Flow Approximation
- CFD + Machine learning for super fast simulations
- What is the role of Artificial Intelligence (AI) or Machine Learning in CFD?
- Supercomputing simulations and machine learning help improve power plant
- When CAE Meets AI: Deep Learning For CFD Simulations
- Machine Learning in Computational Fluid Dynamics
- Studying the nature of turbulence with Neural Concept's deep learning platform
- A case for machine learning in CFD
- Machine Learning for Accelerated Aero-Thermal Design in the Age of Electromobility
- A general purpose list for transitioning to data science and ML
- A compiled list of projects from NVIDIA where AI and CFD were used
- AI for CFD
- 4 Myths about AI in CFD
- Accelerating Product Development with Physics-Informed Neural Networks and NVIDIA Modulus
- Optimize F1 aerodynamic geometries via Design of Experiments and machine learning
- NVIDIA, Rolls-Royce and Classiq Announce Quantum Computing Breakthrough for Computational Fluid Dynamics in Jet Engines
- Develop Physics-Informed Machine Learning Models with Graph Neural Networks
- The AI algorithm reduces design cycles/costs and time-to-market for advanced products
- Closing the gap between High-Performance Computing (HPC) and artificial intelligence (AI)
- Center for Data-Driven Computational Physics
- VinuesaLab
- DeepTurb
- Thuerey Group
- Mechanical and AI LAB (MAIL)
- Karniadakis's CRUNCH group
- MS 6: Machine Learning and Simulation Science
- Special Interest Groups: Machine Learning for Fluid Dynamics
- Fukagata Lab
- OpenFOAM Machine Learning Hackathon
- machine-learning-applied-to-cfd
- Computational-Fluid-Dynamics-Machine-Learning-Examples
- Image Based CFD Using Deep Learning
- Deep-Flow-Prediction
- TensorFlowFoam
- Reduced-order modeling of reacting flows using data-driven approaches - Notebook example for the data driven modeling.
- Tutorial on the Proper Orthogonal Decomposition (POD) by Julien Weiss
- Repository from KTH-FLOW for ML in Fluid Dynamics
- Neural Concepts
- Flowfusic
- byteLAKE - accelerated-cfd-computational-fluid-dynamics-how-does-bytelakes-cfd-suite-work-fea42fd0761e).
- NVIDIA
- NAVASTO
- Nek5000
- OpenFOAM
- PyFr
- Nektar++
- Flexi
- SU2
- code_saturne
- Dolfyn
- Neko
- Snek5000
- CFDOnline
- StackExchange
Programming Languages
Keywords
machine-learning
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