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This course introduces Scientific Machine Learning, beginning with an overview of traditional and modern machine learning methods illustrated with examples from physics. It then transitions to physics-informed approaches, where physical laws, symmetries, and mechanistic models are embedded into

This series is a crash course introduction to a handful of advanced topics designed to tackle the general problem of how to engineer Positive Operator-Valued Measures (POVMs) using observable building blocks, the so-called Instrument Manifold Program. This program emerged from a recent fundamental

The annual Graduate Students’ Conference showcases the diverse research directions at Perimeter Institute, both organized and presented by the students. Our graduate students are invited to share their best work with their fellow PhD students, PSI students and other PI residents interested in

The aim of this course is to explore the main ideas of the statistical physics approach to critical phenomena. We will discuss phase transitions, using the ferromagnetic phase transition and the Ising model as our primary example. The renormalisation group approach will be an important part of this

This is a theoretical physics course that aims to review the basics of theoretical mechanics, special relativity, and classical field theory, with the emphasis on geometrical notions and relativistic formalism, thus setting the stage for the forthcoming courses in Quantum Mechanics, and Quantum

Machine learning has become a very valuable toolbox for scientists including physicists. In this course, we will learn the basics of machine learning with an emphasis on applications for many-body physics. At the end of this course, you will be equipped with the necessary and preliminary tools for

This course teaches basic numerical methods that are widely used across many fields of physics. The course is based on the Julia programming language. Topics include an introduction to Julia, linear algebra, Monte Carlo methods, differential equations, and are based on applications by researchers at

This course teaches basic numerical methods that are widely used across many fields of physics. The course is based on the Python programming language. Topics include an introduction to Python, linear algebra, Monte Carlo methods, root finding, integration, differential equations, and are based on

Pedro has selected 9 papers for this mini-course. Infrared Photons and Gravitons by Weinberg, 1965 Determination of an Operator Algebra for the 2D Ising Model by Kadanoff and Ceva, 1971 Confinement of Quarks by Wilson, 1974, Phenomenological Lagrangians by Weinberg, 1979, Gravitational Effects on

The aim of this course is to explore the main ideas of the statistical physics approach to critical phenomena. We will discuss phase transitions, using the ferromagnetic phase transition and the Ising model as our primary example, with particular emphasis on the renormalisation group approach

This is a theoretical physics course that aims to review the basics of theoretical mechanics, special relativity, and classical field theory, with the emphasis on geometrical notions and relativistic formalism, thus setting the stage for the forthcoming courses in Quantum Mechanics, and Quantum

Machine learning has become a very valuable toolbox for scientists including physicists. In this course, we will learn the basics of machine learning with an emphasis on applications for many-body physics. At the end of this course, you will be equipped with the necessary and preliminary tools for