- Getting Starting with Arc
- How to set a GPU node for ML training in PyTorch
- Tutorial 1
The following are some key references pertaining to the topic of machine learning for physics. This field is rapidly expanding and including an all-encompassing list of reference would be overwhelming and diminish the effectivness of such a resource. The following list includes references on a few of the essential topics covered, discussed, or mentioned during the workshop along with a few introductory references. The presentations also include several references.
- Deep Learning Book
- Dive Into Deep Learning
- Improved Training of Wasserstein GANs
- Attention is All You Need
- Automatic Differentiation of Algorithms
- Evaluating Derivatives: Principles and Techniques of Algorithmic Differentiation
- Artificial neural networks for solving ordinary and partial differential equations
- Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
- Neural Operator: Learning Maps Between Function Spaces With Applications to PDEs
- Fourier Neural Operator for Parametric Partial Differential Equations
- Neural Ordinary Differential Equations
- Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)