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)

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📂 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                   

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📌 AI in CAE Topics

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

• Gilbert Strang’s Linear Algebra Lectures – My personal all-time favorite for mastering matrices and transformations.
• 3Blue1Brown - Essence of Linear Algebra – Fantastic visual intuition for linear algebra concepts.
• 3Blue1Brown - Essence of Calculus – A must-watch for an intuitive grasp of calculus.
• Steve Brunton’s Probability & Statistics – Great for understanding probability in an applied manner.
• Dan Fleisch - What’s a Tensor? – The best quick introduction to tensors.
• Tensors Explained – A deeper dive into tensor concepts.

➡️ For a detailed breakdown, refer to 00_Math_Physics_Foundations.md

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1. Machine Learning and Deep Learning Fundamentals for CAE

• AI For Everyone - Andrew Ng (Coursera)
• Stanford CS229: Machine Learning Course
• DeepLearning.AI Specialization (Coursera)
• Hands-On Machine Learning with Scikit-Learn, Keras & TensorFlow - Aurélien Géron
• Deep Learning by Ian Goodfellow, Yoshua Bengio, Aaron Courville

➡️ For a detailed breakdown, refer to 01_ML_DeepLearning_CAE.md

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2. Geometric Deep Learning in Engineering Simulations

• Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, Gauges" by Bronstein et al
• AMMI 2022 Course "Geometric Deep Learning"
• PyTorch Geometric Tutorials
• Geometric Deep Learning" by Michael Bronstein

➡️ For a detailed breakdown, refer to 02_Geometric_DeepLearning.md

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

• DeepXDE: Library for Scientific Machine Learning
• PINNs Tutorial by Raissi, Perdikaris, Karniadakis
• Steve Brunton's lectures

➡️ For a detailed breakdown, refer to 03_PINNs_CAE.md

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4. GANs and Generative AI Applications in Engineering Design

• GANs Specialization - Coursera
• Generative Adversarial Networks (GANs) in Theory and PyTorch - Tutorial
• Generative Adversarial Networks with Python - Jason Brownlee

➡️ For a detailed breakdown, refer to 04_Generative_AI_CAE.md

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5. Reinforcement Learning for Optimization in CAE

• David Silver's Reinforcement Learning Course (UCL)
• DeepMind X UCL Lectures
• Spinning Up in Deep RL (OpenAI)
• Stable Baselines

➡️ For a detailed breakdown, refer to 05_RL_CAE.md

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6. Self-Supervised Learning Techniques for Simulation Data

• Self-Supervised Learning: A Survey
• Yann LeCun's Presentation in Youtube
• Lilian Weng's Self-Supervised Learning Blog

➡️ For a detailed breakdown, refer to 06_SSL_Simulation_Data.md

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7. Python Programming Tools/Libraries for CAE Integration

• TensorFlow
• PyTorch
• PyTorch Geometric
• SciPy
• OpenFOAM
• PyVista: 3D plotting & mesh analysis wrapper for VTK library
• Lasso: Python library for dyna files, femzip, diffcrash and dimensionality reduction functionalities.
• ANSA Scripting Tutorials

➡️ For a detailed breakdown, refer to 07_Python_Tools_CAE.md

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8. Best Practices & Case Studies

• Cadence's Generative AI Portfolio using Geometric Deep Learning
• Altair's physicsAI Application in CAE
• Engineering Intelligence with Neural Concept Shape

➡️ For a detailed breakdown, refer to 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.