{"id":26790576,"url":"https://github.com/revankumard/cae_ai_resources","last_synced_at":"2025-03-29T14:29:17.991Z","repository":{"id":276286515,"uuid":"928812220","full_name":"RevanKumarD/cae_ai_resources","owner":"RevanKumarD","description":"This repository provides a comprehensive list of resources for integrating Artificial Intelligence (AI) into Computer-Aided Engineering (CAE). 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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)\n\n---\n\n## 📂 Repository Structure\n\n```\n│── 00_Math_Physics_Foundations.md # Mathematical \u0026 Physics Foundations\n│── 01_ML_DeepLearning_CAE.md      # Machine Learning \u0026 Deep Learning Fundamentals\n│── 02_Geometric_DeepLearning.md   # Geometric Deep Learning in CAE\n│── 03_PINNs_CAE.md                # Physics-Informed Neural Networks (PINNs)\n│── 04_Generative_AI_CAE.md        # GANs and Generative AI for Engineering\n│── 05_RL_CAE.md                   # Reinforcement Learning for CAE Optimization\n│── 06_SSL_Simulation_Data.md      # Self-Supervised Learning for Simulation Data\n│── 07_Python_Tools_CAE.md         # Python Libraries \u0026 Tools for CAE\n│── 08_Best_Practices_CaseStudies.md # Best Practices and Case Studies                   \n```\n\n---\n\n## 📌 AI in CAE Topics\n\n\n### **0. [Math \u0026 Physics] Foundational Concepts in CAE - Personal Recommendations**\n- [Gilbert Strang’s Linear Algebra Lectures](https://www.youtube.com/playlist?list=PL49CF3715CB9EF31D) – My personal all-time favorite for mastering matrices and transformations.\n- [3Blue1Brown - Essence of Linear Algebra](https://www.youtube.com/playlist?list=PLZHQObOWTQDPD3MizzM2xVFitgF8hE_ab) – Fantastic visual intuition for linear algebra concepts.\n- [3Blue1Brown - Essence of Calculus](https://www.youtube.com/playlist?list=PLZHQObOWTQDMsr9K-rj53DwVRMYO3t5Yr) – A must-watch for an intuitive grasp of calculus.\n- [Steve Brunton’s Probability \u0026 Statistics](https://youtu.be/sQqniayndb4?si=WXaE3EaK8pcONvSW) – Great for understanding probability in an applied manner.\n- [Dan Fleisch - What’s a Tensor?](https://www.youtube.com/watch?v=f5liqUk0ZTw) – The best quick introduction to tensors.\n- [Tensors Explained](https://www.youtube.com/watch?v=CliW7kSxxWU) – A deeper dive into tensor concepts.\n\n➡️ *For a detailed breakdown, refer to [00_Math_Physics_Foundations.md](00_Math_Physics_Foundations.md)*\n\n\n---\n\n### **1. Machine Learning and Deep Learning Fundamentals for CAE**\n- [AI For Everyone - Andrew Ng (Coursera)](https://www.coursera.org/learn/ai-for-everyone)\n- [Stanford CS229: Machine Learning Course](https://www.youtube.com/playlist?list=PLoROMvodv4rMiGQp3WXShtMGgzqpfVfbU)\n- [DeepLearning.AI Specialization (Coursera)](https://www.coursera.org/specializations/deep-learning)\n- [Hands-On Machine Learning with Scikit-Learn, Keras \u0026 TensorFlow - Aurélien Géron](https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/)\n- [Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville](https://www.deeplearningbook.org/)\n\n➡️ *For a detailed breakdown, refer to [01_ML_DeepLearning_CAE.md](01_ML_DeepLearning_CAE.md)*\n\n---\n\n### **2. Geometric Deep Learning in Engineering Simulations**\n- [Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, Gauges\" by Bronstein et al](https://arxiv.org/abs/2104.13478)\n- [AMMI 2022 Course \"Geometric Deep Learning\"](https://geometricdeeplearning.com/lectures/)  \n- [PyTorch Geometric Tutorials](https://pytorch-geometric.readthedocs.io/en/latest/get_started/colabs.html)\n- [Geometric Deep Learning\" by Michael Bronstein](https://www.youtube.com/watch?v=hROSXAY2JBc)\n\n➡️ *For a detailed breakdown, refer to [02_Geometric_DeepLearning.md](02_Geometric_DeepLearning.md)*\n\n---\n\n### **3. Physics-Informed Neural Networks (PINNs) for CAE Workflows**\n- [DeepXDE: Library for Scientific Machine Learning](https://github.com/lululxvi/deepxde)\n- [PINNs Tutorial by Raissi, Perdikaris, Karniadakis](https://maziarraissi.github.io/PINNs/)  \n- [Steve Brunton's lectures](https://www.youtube.com/@Eigensteve/search?query=PINNs)\n\n➡️ *For a detailed breakdown, refer to [03_PINNs_CAE.md](03_PINNs_CAE.md)*\n\n---\n\n### **4. GANs and Generative AI Applications in Engineering Design**\n- [GANs Specialization - Coursera](https://www.coursera.org/specializations/generative-adversarial-networks-gans)  \n- [Generative Adversarial Networks (GANs) in Theory and PyTorch - Tutorial](https://pytorch.org/tutorials/beginner/dcgan_faces_tutorial.html)\n- [Generative Adversarial Networks with Python - Jason Brownlee](https://machinelearningmastery.com/start-here/#gans) \n\n➡️ *For a detailed breakdown, refer to [04_Generative_AI_CAE.md](04_Generative_AI_CAE.md)*\n\n---\n\n### **5. Reinforcement Learning for Optimization in CAE**\n- [David Silver's Reinforcement Learning Course (UCL)](https://www.davidsilver.uk/teaching/)  \n- [DeepMind X UCL Lectures](https://www.youtube.com/playlist?list=PLqYmG7hTraZDVH599EItlEWsUOsJbAodm)\n- [Spinning Up in Deep RL (OpenAI)](https://spinningup.openai.com/en/latest/)\n- [Stable Baselines](https://stable-baselines.readthedocs.io/en/master/)\n\n➡️ *For a detailed breakdown, refer to [05_RL_CAE.md](05_RL_CAE.md)*\n\n---\n\n### **6. Self-Supervised Learning Techniques for Simulation Data**\n- [Self-Supervised Learning: A Survey](https://arxiv.org/pdf/2301.05712)\n- [Yann LeCun's Presentation in Youtube](https://www.youtube.com/results?search_query=Yann+LeCun+on+Self-Supervised+Learning)\n- [Lilian Weng's Self-Supervised Learning Blog](https://lilianweng.github.io/posts/2019-11-10-self-supervised/)  \n\n➡️ *For a detailed breakdown, refer to [06_SSL_Simulation_Data.md](06_SSL_Simulation_Data.md)*\n\n---\n\n### **7. Python Programming Tools/Libraries for CAE Integration**\n- [TensorFlow](https://www.tensorflow.org/)\n- [PyTorch](https://pytorch.org/)\n- [PyTorch Geometric](https://pytorch-geometric.readthedocs.io/en/latest/)\n- [SciPy](https://scipy.org/)\n- [OpenFOAM](https://www.openfoam.com/)\n- **PyVista**: 3D plotting \u0026 mesh analysis wrapper for VTK library\n- **Lasso**: Python library for dyna files, femzip, diffcrash and dimensionality reduction functionalities.\n- [ANSA Scripting Tutorials](https://www.youtube.com/@Beta-caeGr/search?query=Scripting)\n\n➡️ *For a detailed breakdown, refer to [07_Python_Tools_CAE.md](07_Python_Tools_CAE.md)*\n\n---\n\n### **8. Best Practices \u0026 Case Studies**\n- [Cadence's Generative AI Portfolio using Geometric Deep Learning](https://www.cadence.com/en_US/home/explore/geometric-deep-learning.html)\n- [Altair's physicsAI Application in CAE](https://altair.com/ai-powered-engineering)\n- [Engineering Intelligence with Neural Concept Shape](https://www.neuralconcept.com/customer-stories)\n\n➡️ *For a detailed breakdown, refer to [08_Best_Practices_CaseStudies.md](08_Best_Practices_CaseStudies.md)*\n\n---\n\n## 🚀 Contribute\nIf you have additional resources, please contribute via a pull request!\n\n## 📜 License\nThis repository is licensed under the MIT License.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frevankumard%2Fcae_ai_resources","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Frevankumard%2Fcae_ai_resources","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Frevankumard%2Fcae_ai_resources/lists"}