GDS Short Course on Data Science for Physicists

Data science is playing an ever increasing role in physics. In this two day tutorial, we will introduce data science as it applies to a variety of fields in physics. The first day of the course is an introduction to the fields of data science and machine learning (ML) as they apply to physics data. We will then provide an introduction to machine learning, including both regression and classification algorithms. This session will explain why neural networks work and describe the practical steps needed to train a model, such as feature engineering, hyperparameter tuning, and validation. We will conclude the first day of the tutorial with an introduction to unsupervised learning techniques (including clustering and random forests), as well as a session which will introduce both neural networks (NNs) and convolutional networks (CNNs). The second day of this course will provide sessions on advanced topics in data science and machine learning. The first two sessions will cover graph neural networks (GNNs) and large language models (LLMs), focusing on their applications to physics. The final four sessions of the tutorial will cover a range of applications of both machine learning and data science. The session “Assessing Training Data: Material Data APIs” will cover accessing large, online databases of materials data to use as training data for machine learning algorithms. The session “Introduction to neural-network quantum states (NQS)” aims to provide a clear understanding of NQS and their broader applications in quantum many-body physics by introducing the theoretical and computational background necessary for constructing NQS, focusing on the quantum harmonic oscillator. The third session of the afternoon, “Using Data Science to Understand Complexity in Soft Matter Systems”, will discuss recent applications of data science and machine learning to understanding complexity in soft matter systems. Finally, the session “Applications of Machine Learning to Biology” will focus on using AI to build “mechanistic foundation models” capable of physics simulations of the brain and the body of the fruit fly.

If you have already registered for the meeting but would like to add the Short Course, please contact registrar@aps.org