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https://github.com/revankumard/cae_ai_resources

This repository provides a comprehensive list of resources for integrating Artificial Intelligence (AI) into Computer-Aided Engineering (CAE). It includes categorized tutorials, courses, research papers, open-source tools, case studies, and best practices across various AI techniques applied to CAE.
https://github.com/revankumard/cae_ai_resources

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This repository provides a comprehensive list of resources for integrating Artificial Intelligence (AI) into Computer-Aided Engineering (CAE). It includes categorized tutorials, courses, research papers, open-source tools, case studies, and best practices across various AI techniques applied to CAE.

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README 

          
# AI in Computer-Aided Engineering (CAE) Resources



This repository provides a **comprehensive list of resources** for integrating **Artificial Intelligence (AI) into Computer-Aided Engineering (CAE)**. It includes categorized tutorials, courses, research papers, open-source tools, case studies, and best practices across various AI techniques applied to CAE. (Refined using AI)



---



## 📂 Repository Structure



```

│── 00_Math_Physics_Foundations.md # Mathematical & Physics Foundations

│── 01_ML_DeepLearning_CAE.md      # Machine Learning & Deep Learning Fundamentals

│── 02_Geometric_DeepLearning.md   # Geometric Deep Learning in CAE

│── 03_PINNs_CAE.md                # Physics-Informed Neural Networks (PINNs)

│── 04_Generative_AI_CAE.md        # GANs and Generative AI for Engineering

│── 05_RL_CAE.md                   # Reinforcement Learning for CAE Optimization

│── 06_SSL_Simulation_Data.md      # Self-Supervised Learning for Simulation Data

│── 07_Python_Tools_CAE.md         # Python Libraries & Tools for CAE

│── 08_Best_Practices_CaseStudies.md # Best Practices and Case Studies                   

```



---



## 📌 AI in CAE Topics



### **0. [Math & Physics] Foundational Concepts in CAE - Personal Recommendations**

- [Gilbert Strang’s Linear Algebra Lectures](https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D) – My personal all-time favorite for mastering matrices and transformations.

- [3Blue1Brown - Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) – Fantastic visual intuition for linear algebra concepts.

- [3Blue1Brown - Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) – A must-watch for an intuitive grasp of calculus.

- [Steve Brunton’s Probability & Statistics](https://youtu.be/sQqniayndb4?si=WXaE3EaK8pcONvSW) – Great for understanding probability in an applied manner.

- [Dan Fleisch - What’s a Tensor?](https://www.youtube.com/watch?v=f5liqUk0ZTw) – The best quick introduction to tensors.

- [Tensors Explained](https://www.youtube.com/watch?v=CliW7kSxxWU) – A deeper dive into tensor concepts.



➡️ *For a detailed breakdown, refer to [00_Math_Physics_Foundations.md](00_Math_Physics_Foundations.md)*



---



### **1. Machine Learning and Deep Learning Fundamentals for CAE**

- [AI For Everyone - Andrew Ng (Coursera)](https://www.coursera.org/learn/ai-for-everyone)

- [Stanford CS229: Machine Learning Course](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)

- [DeepLearning.AI Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning)

- [Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)

- [Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville](https://www.deeplearningbook.org/)



➡️ *For a detailed breakdown, refer to [01_ML_DeepLearning_CAE.md](01_ML_DeepLearning_CAE.md)*



---



### **2. Geometric Deep Learning in Engineering Simulations**

- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, Gauges" by Bronstein et al](https://arxiv.org/abs/2104.13478)

- [AMMI 2022 Course "Geometric Deep Learning"](https://geometricdeeplearning.com/lectures/)  

- [PyTorch Geometric Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)

- [Geometric Deep Learning" by Michael Bronstein](https://www.youtube.com/watch?v=hROSXAY2JBc)



➡️ *For a detailed breakdown, refer to [02_Geometric_DeepLearning.md](02_Geometric_DeepLearning.md)*



---



### **3. Physics-Informed Neural Networks (PINNs) for CAE Workflows**

- [DeepXDE: Library for Scientific Machine Learning](https://github.com/lululxvi/deepxde)

- [PINNs Tutorial by Raissi, Perdikaris, Karniadakis](https://maziarraissi.github.io/PINNs/)  

- [Steve Brunton's lectures](https://www.youtube.com/@Eigensteve/search?query=PINNs)



➡️ *For a detailed breakdown, refer to [03_PINNs_CAE.md](03_PINNs_CAE.md)*



---



### **4. GANs and Generative AI Applications in Engineering Design**

- [GANs Specialization - Coursera](https://www.coursera.org/specializations/generative-adversarial-networks-gans)  

- [Generative Adversarial Networks (GANs) in Theory and PyTorch - Tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)

- [Generative Adversarial Networks with Python - Jason Brownlee](https://machinelearningmastery.com/start-here/#gans) 



➡️ *For a detailed breakdown, refer to [04_Generative_AI_CAE.md](04_Generative_AI_CAE.md)*



---



### **5. Reinforcement Learning for Optimization in CAE**

- [David Silver's Reinforcement Learning Course (UCL)](https://www.davidsilver.uk/teaching/)  

- [DeepMind X UCL Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)

- [Spinning Up in Deep RL (OpenAI)](https://spinningup.openai.com/en/latest/)

- [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/)



➡️ *For a detailed breakdown, refer to [05_RL_CAE.md](05_RL_CAE.md)*



---



### **6. Self-Supervised Learning Techniques for Simulation Data**

- [Self-Supervised Learning: A Survey](https://arxiv.org/pdf/2301.05712)

- [Yann LeCun's Presentation in Youtube](https://www.youtube.com/results?search_query=Yann+LeCun+on+Self-Supervised+Learning)

- [Lilian Weng's Self-Supervised Learning Blog](https://lilianweng.github.io/posts/2019-11-10-self-supervised/)  



➡️ *For a detailed breakdown, refer to [06_SSL_Simulation_Data.md](06_SSL_Simulation_Data.md)*



---



### **7. Python Programming Tools/Libraries for CAE Integration**

- [TensorFlow](https://www.tensorflow.org/)

- [PyTorch](https://pytorch.org/)

- [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)

- [SciPy](https://scipy.org/)

- [OpenFOAM](https://www.openfoam.com/)

- **PyVista**: 3D plotting & mesh analysis wrapper for VTK library

- **Lasso**: Python library for dyna files, femzip, diffcrash and dimensionality reduction functionalities.

- [ANSA Scripting Tutorials](https://www.youtube.com/@Beta-caeGr/search?query=Scripting)



➡️ *For a detailed breakdown, refer to [07_Python_Tools_CAE.md](07_Python_Tools_CAE.md)*



---



### **8. Best Practices & Case Studies**

- [Cadence's Generative AI Portfolio using Geometric Deep Learning](https://www.cadence.com/en_US/home/explore/geometric-deep-learning.html)

- [Altair's physicsAI Application in CAE](https://altair.com/ai-powered-engineering)

- [Engineering Intelligence with Neural Concept Shape](https://www.neuralconcept.com/customer-stories)



➡️ *For a detailed breakdown, refer to [08_Best_Practices_CaseStudies.md](08_Best_Practices_CaseStudies.md)*



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## 🚀 Contribute

If you have additional resources, please contribute via a pull request!



## 📜 License

This repository is licensed under the MIT License.