MITx Course
Computational Data Science in Physics: Explore realistic, contemporary examples of how computational methods apply to physics research. Course developed by Phil Harris, Ike Chuang, and Alex Shvonski.
- Free access to all content
- Self-paced, 6-week courses
- Course content available at any time, certificate available (with fee) when course is active
- GitHub repositories
Module 1
In this first module, you will analyze LIGO data, detect a gravitational wave signal, and fit this signal within a physical model, among other objectives, using Jupyter notebooks.
Module 2
In this module, topics include hypothesis testing, semi-parameteric methods, and deep learning. In the Final Project, you will analyze LHC data to measure properties of the W boson and Z boson.
Module 3
In this third module, learners will learn about how to simulate physics processes. This will culminate in simulations of lattice models including the Ising model, with relevance to contemporary lattice QCD.
MIT Interdisciplinary PhD
IAIFI has partnered with the MIT Physics department and the MIT Statistics and Data Science Center to establish an interdisciplinary PhD program in Physics, Statistics and Data Science, which is designed to provide students with the highest level of competency in 21st century statistics, enabling doctoral students across MIT to better integrate computation and data analysis into their PhD thesis research.
- Open to PhD students in the MIT Physics department
- In addition to satisfying all of the requirements of the Physics PhD, students take one subject each in probability, statistics, computation and statistics, and data analysis, as well as the Doctoral Seminar in Statistics, and they write a dissertation in Physics utilizing statistical methods.
- For access to the selection form or for further information, please contact the IDSS Academic Office at idss_academic_office@mit.edu.
University Courses
Many IAIFI faculty are developing courses at the intersection of physics and computer science. If you are an IAIFI faculty member with a course you’d like to share, or if you are a professor at another university working on similar courses and are interested in sharing resources, email IAIFI.
MIT
- 8.16/8.316: Data Science in Physics: Prof. Phil Harris
Aims to present modern computational methods by providing realistic, contemporary examples of how these computational methods apply to physics research. Designed around research modules in which each module provides experience with a specific scientific challenge. Modules include: analyzing LIGO open data; measuring electroweak boson to quark decays; understanding the cosmic microwave background; and lattice QCD/Ising model. Experience in Python helpful but not required. Lectures are viewed outside of class; in-class time is dedicated to problem- solving and discussion. Students taking graduate version will complete additional assignments.
- 6.S966 Symmetry and its Application to Machine Learning and Scientific Computing: Prof. Tess Smidt
Introduces the use of group representation theory to construct symmetry-preserving algorithms for machine learning. Emphases the connection between topics in math and physics and machine learning. Students will implement core mathematical concepts in code to build algorithms that can operate on graphs, geometry, scientific data, and other structured data to preserve the symmetries of these domains. Topics covered include: Euclidean and permutation groups, group representations: regular, reducible, and irreducible, tensor products, statistics and sampling of group representation vector spaces, and symmetry-breaking mechanisms.
- 2.c01/2.c51 Physical Systems Modeling and Design using Machine Learning: Prof. George Barbastathis and Prof. Sherrie Wang
Introduces the fundamentals and explores applications at the fruitful intersection between the physical sciences and machine learning. While relying heavily on the basics of machine learning taught in the gateway class 6.c01 / 6.c51, we emphasize how these superimpose on dynamical principles from physical, chemical, biological and societal systems. The course will be organized as: one hour common lecture for the undergraduate and graduate students, followed by one hour of separate recitaIons. All students will parIcipate in team projects on topics of their own choice, in consultation with the instructors.
Northeastern
- PHYS 7321 Computational Physics: Prof. Jim Halverson
Covers basic numerical methods for differentiation, integration, and matrix operations used in linear algebra problems, discrete Fourier transforms, and standard and stochastic ordinary and partial differential equations. Specific applications of these methods may include classical chaos, computation of eigenstates of simple quantum systems, classical phase transitions, boundary value problems, pattern formation, and molecular dynamics and classical/quantum Monte Carlo methods to simulate the equilibrium and nonequilibrium properties of condensed phases.