This course provides realistic, contemporary examples of how computational methods apply to physics research. 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.
This course provides realistic, contemporary examples of how computational methods apply to physics research. 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.
Computational methods are a critical component of many fields of physics research. With the rise of deep learning and the development of large-scale computational facilities, the impact of computation has become increasingly important. Physics research in a broad range of fields has been rapidly accelerated due to emerging numerical techniques that have allowed for more comprehensive data analysis and increased computational complexity of physical phenomena. Much of the recent work in physics underpins the emerging field of Data Science and has helped to cultivate critical problems with solutions that cross-cut many areas of research.
Numerical Differential equations, Finite approximations and numerical integrators, Physics informed Machine Learning, Multi-body simulation, Finite Element Simulation, Monte Carlo, Deep Learning based sampling strategies, Markov Chain Monte Carlo, Simulation Based Inference/Likelihood free inference, AI-based anomaly detection, Lattice QCD, Normalizing Flows.
Philip Harris joined the MIT faculty in 2017. Born in Sao Paulo, he received his B.S in Physics from Caltech in 2005, and his Ph.D from MIT in 2011 on research performed at CERN with the Large Hadron Collider. From 2011-2013, Philip was a CERN fellow working on the Higgs discovery. From 2014-2017, he was a CERN staff scientist working on dark matter searches at the CMS experiment. Philip is one of the founders of the Fast Machine Learning Organization. Also, he is currently the experimental coordinator of The Institute for AI and Fundamental Interactions (IAIFI), and he is the deputy director for the Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3) Institute.
Prof. Chuang is a pioneer in the field of quantum information science. His experimental realization of two, three, five, and seven quantum bit quantum computers using nuclear spins in molecules provided the first laboratory demonstrations of many important quantum algorithms, including Shor’s quantum factoring algorithm. The error correction, algorithmic cooling, and entanglement manipulation techniques he developed provide new ways to obtain complete quantum control over light and matter, and lay a foundation for possible large-scale quantum information processing systems.
Prof. Chuang came to MIT in 2000 from IBM, where he was a research staff member. He received his doctorate in Electrical Engineering from Stanford University, where he was a Hertz Foundation Fellow. Prof. Chuang also holds two bachelors and one masters degrees in Physics and Electrical Engineering from MIT, and was a post-doctoral fellow at Los Alamos National Laboratory and the University of California at Berkeley. He is the author, together with Michael Nielsen, of the textbook Quantum Computation and Quantum Information.
Jesse Thaler is a theoretical particle physicist who fuses techniques from quantum field theory and machine learning to address outstanding questions in fundamental physics. His current research is focused on maximizing the discovery potential of the Large Hadron Collider (LHC) through new theoretical frameworks and novel data analysis techniques. Prof. Thaler is an expert in jets, which are collimated sprays of particles that are copiously produced at the LHC, and he studies the substructure of jets to enhance the search for new phenomena and illuminate the dynamics of gauge theories. He is also interested in new strategies to probe the nature of dark matter at the LHC and beyond, as well as in the theoretical structures and experimental signatures of supersymmetry.
Alex Shvonski is a Postdoc in the Department of Physics at MIT, currently working on developing online classes. He also works with introductory physics courses at MIT, most recently creating and running in-class experiments. Alex is interested in physics education research and developing effective ways for students to interactively engage with content, for instance through hands-on experiments or simulations. He received his Ph.D. in Physics from Boston College working on plasmonics.