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Lecture material for machine learning applied to computational fluid mechanics
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 This lecture was funded previously by the [foundation for innovation in higher education](https://stiftung-hochschullehre.de/). # Machine learning in computational fluid dynamics This repository contains resources accompanying the lecture [machine learning in fluid dynamics](https://tu-dresden.de/ing/maschinenwesen/ism/psm/studium/lehrveranstaltungen/maschinelles-lernen-in-der-stroemungsmechanik/index) provided by the Institute of Fluid Mechanics at TU Dresden. **Note that slides, notebooks, and other resources will be regularly updated throughout the term.** ## Lectures If equations in the lecture notebooks do not get rendered properly on Github, download the notebook and open it using `jupyter-lab` (refer to the first exercise session for an overview of dependencies and installation instructions). | # | topic | slides | notebook | |--:|:------|:------:|:---------| | 1 | Course overview and motivation | [link](https://andreweiner.github.io/ml-cfd-slides/ml_cfd_intro.html) | [view](./notebooks/ml_cfd_intro.ipynb) | | 2 | Finite-volume-based simulations in a nutshell | [link](https://andreweiner.github.io/ml-cfd-slides/cfd_intro.html) | [view](./notebooks/cfd_intro.ipynb) | | 3 | Introduction to machine learning | [link](https://andreweiner.github.io/ml-cfd-slides/ml_intro.html) | [view](./notebooks/ml_intro.ipynb) | | 4 | Surrogate modeling for discrete predictions | [link](https://andreweiner.github.io/ml-cfd-slides/bubble_path_classification.html) | [view](./notebooks/bubble_path_classification.ipynb) | | 5 | Surrogate modeling for continuous predictions | [link](https://andreweiner.github.io/ml-cfd-slides/mass_transfer_regression.html) |[view](./notebooks/mass_transfer_regression.ipynb) | | 6 | Analyzing coherent structures| [link](https://andreweiner.github.io/ml-cfd-slides/coherent_structures_dim_reduction.html) | [view](./notebooks/coherent_structures_dim_reduction.ipynb) | | 7 | Reduced-order modeling of flow fields | [link](https://andreweiner.github.io/ml-cfd-slides/cylinder_rom.html) | [view](./notebooks/cylinder_rom.ipynb) | | 8 | Optimal open-loop control | [link](https://andreweiner.github.io/ml-cfd-slides/cylinder_bayesian_opt.html) | [view](./notebooks/cylinder_bayesian_opt.ipynb) | | 9 | Closed-loop control using DRL | [link](https://andreweiner.github.io/ml-cfd-slides/cylinder_drl.html) | [view](./notebooks/cylinder_drl.ipynb) | ## Exercises ### Prerequisites The exercises are designed for native Linux operating systems like Ubuntu (recommended). They may also work on Windows Subsystem for Linux (WSL). To set up your system for the exercises, refer to the notebook accompanying exercise session 1. ### Exercise sessions | # | topic | notebook | |--:|:------|:---------| | 0 | Course-specific Python refresher | [view](./notebooks/python_intro.ipynb) | | 1 | Setting up your system | [view](./notebooks/system_setup.ipynb) | | 2 | End-to-end simulations in OpenFOAM and Basilisk | [view](./notebooks/cfd_intro_exercise.ipynb) | | 3 | End-to-end machine learning project in PyTorch | [view](./notebooks/ml_intro_exercise.ipynb) | | 4 | Building a robust path regime classification model | [view](./notebooks/bubble_path_classification_exercise.ipynb)| | 5 | Computing highly accurate mass transfer at rising bubbles | [view](./notebooks/mass_transfer_regression_exercise.ipynb) | | 6 | Analyzing coherent structures with POD and DMD| [view](./notebooks/coherent_structures_dim_reduction_exercise.ipynb) | | 7 | Creating a reduced-order model using CNM | [view](./notebooks/cylinder_rom_exercise.ipynb) | | 8 | Optimal open-loop control of the flow past a cylinder| [view](./notebooks/cylinder_bayesian_opt_exercise.ipynb) | | 9 | Closed-loop control of the flow past a cylinder | [view](./notebooks/cylinder_drl_exercise.ipynb) | ## Datasets Both exercises and lectures sometimes require datasets. Usually, there are instructions how to create or extract the data yourself. Fo
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matched fp:a1a945a8efa369c1, llm:Repository description: 'Lecture material for machine learning applied to computational fluid mechanics' (Jupyter Notebook language).
matched fp:a1a945a8efa369c1, llm:Repository description: 'Lecture material for machine learning applied to computational fluid mechanics' (Jupyter Notebook language).
matched fp:a1a945a8efa369c1, llm:Repository description: 'Lecture material for machine learning applied to computational fluid mechanics' (Jupyter Notebook language).