openhackathons-org/End-to-End-AI-for-Science
📚 Tutorialopenhackathons-org
A comprehensive collection of tutorials and materials for applying AI to scientific discovery and high-performance computing.
This repository serves as a curated educational resource for the intersection of AI and scientific computing. It focuses on 'AI for Science' (AI4Science), a rapidly evolving field that leverages deep learning to accelerate simulations that were previously computationally prohibitive. The repository includes detailed tutorials on Physics-Informed Neural Networks (PINNs), which embed physical laws directly into the loss functions of neural networks to ensure physically consistent predictions.
Key technical highlights include implementations for FourCastNet, a transformer-based model for global weather forecasting, and NVIDIA Modulus, a framework for developing physics-ML models. The materials are structured as Jupyter Notebooks, providing a step-by-step guide for setting up environments, training models on scientific datasets, and deploying them within HPC (High-Performance Computing) infrastructures. By covering topics from Navier-Stokes equations to climate modeling, the project equips researchers with the tools to replace or augment traditional numerical solvers with high-performance AI surrogates.
💡Highlights
- ├─Covers PINNs & Navier-Stokes
- ├─Includes FourCastNet tutorials
- └─NVIDIA Modulus integration
🎯For
- ├─AI Researchers
- ├─Computational Scientists
- └─HPC Engineers