Format results
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Talk
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Lecture - Machine Learning, PHYS 777
Mohamed Hibat Allah University of Waterloo
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Talk
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Lecture - Numerical Methods, PHYS 777
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Erik Schnetter Perimeter Institute for Theoretical Physics
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Dustin Lang Perimeter Institute for Theoretical Physics
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Talk
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Lecture - Statistical Physics, PHYS 602
Emilie Huffman Perimeter Institute for Theoretical Physics
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Talk
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Lecture - Beautiful Papers
Pedro Vieira Perimeter Institute for Theoretical Physics
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Talk
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Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24100001 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
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Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090013 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090012 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090011 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090010 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090009 -
Lecture - Classical Physics, PHYS 776
Aldo Riello Perimeter Institute for Theoretical Physics
PIRSA:24090008
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Talk
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Machine Learning Lecture
Damian Pope Perimeter Institute for Theoretical Physics
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Machine Learning Lecture
Mohamed Hibat Allah University of Waterloo
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Talk
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Talk
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Dark Matter and Particle Physics
Luna Zagorac Perimeter Institute for Theoretical Physics
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Cosmology
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Matthew Johnson York University
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Jessica Muir University of Cincinnati
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Talk
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Looking for Quantum-Classical Gaps in Causal Structures
Marina Maciel Ansanelli Perimeter Institute for Theoretical Physics
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Geometry of Process Matrices
Fionnuala Ni Chuireain Institute of Photonic Sciences (ICFO)
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Improving 3D Codes under Biased Noise
Eric Huang University of Maryland, College Park
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Illuminating the pair-instability supernova mass gap with super-kilonovae
Aman Agarwal University of Greifswald
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Reflecting scalar fields in numerical relativity
Conner Dailey Friedrich Schiller University Jena
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Talk
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Welcome and Opening Remarks
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Matthew Johnson York University
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Cheng-Ju Lin University of Maryland, College Park
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Tsung-Cheng (Peter) Lu
Tsung-Cheng Lu (Peter) University of Maryland, College Park
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Roland Bittleson
Roland Bittleston Perimeter Institute for Theoretical Physics
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Suvodip Mukherjee
Suvodip Mukherjee Tata Institute of Fundamental Research (TIFR)
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Talk
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Welcome and Opening Remarks
Anna Heffernan University of the Balearic Islands
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Self force review
Maarten van de Meent Max Planck Institute for Gravitational Physics - Albert Einstein Institute (AEI)
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Discontinuous collocation methods and self-force applications
Charalampos Markakis Queen Mary University of London
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Conformal numerical method for self force applications in the time domain
Lidia Joana Gomes Da Silva Queen Mary University of London
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Kerr self-force via elliptic PDEs: Background and theory (part 1)
Nami Nishimura State University of New York (SUNY)
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Kerr self-force via elliptic PDEs: Numerical methods (part 2)
Thomas Osburn State University of New York (SUNY)
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A multi-mode time-domain surrogate model for gravitational wave signals from comparable to extreme mass-ratio black hole binaries
Tousif Islam University of Massachusetts Dartmouth
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Fast Self-Forced Inspirals into a Rotating Black Hole
Philip Lynch National University of Ireland
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Talk
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PSI 2019/2020 - Machine Learning for Many-Body Physics - Lecture 2
Mohamed Hibat Allah University of Waterloo
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PSI 2019/2020 - Machine Learning for Many-Body Physics - Lecture 1
Mohamed Hibat Allah University of Waterloo
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Machine Learning (Elective), PHYS 777, February 24 - March 28, 2025
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 starting your own machine learning projects. Instructor: Mohamed Hibat Allah Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses -
Numerical Methods (Core), PHYS 777-, January 6 - February 5, 2025
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 Perimeter. The course will also teach principles of software engineering ensuring reproducible results Instructor: Erik Schnetter/Dustin Lang/Subhayan Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses -
Statistical Physics (Core), PHYS 602, October 7 - November 6, 2024
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. Instructor: Emilie Huffman / Maite Dupuis Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses -
Beautiful Papers - October 7, 2024 - January 31, 2025
Pedro will lead a mini-course (no credit) at Perimeter Institute. To express your interest in attending please complete this registration form (https://forms.office.com/r/nDQ6SDxSR4) by noon on Thursday, October 3. 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 and of Vacuum Decay by Coleman and De Luccia, 1980 -Classical and Quantum Gravity Effects from Planckian Energy Superstring Collisions by Amati, Ciafaloni and Veneziano, 1987 -Quantum Spin Chains and The Haldane Gap by Affleck, 1988 -The Large N Limit of SFTs and Supergravity by Maldacena, 1997 -Entanglement Entropy and Quantum Field Theory by Calabrese and Cardy, 2008 If you think there are super nice papers that are missing that could be better than some of these in a particular topic please contact Pedro directly (pvieira@perimeterinstitute.ca). The format will be: A] Pedro will give a 1h30m lecture about one of these papers on a Monday or a Friday (the Monday or Friday right after B] unless there is a holiday) B] One week later, X students give a presentation of Y minutes about important recent papers that refer/are related to that paper. At the end of this lecture we give feedback about those presentations. What is X and what is Y depends on how many students volunteer to present. Of course, people who just want to attend without presenting are also most welcome! Location: Alice Room, 3rd Floor, Perimeter Institute, 31 Caroline St N, Waterloo Recording/Zoom Details: Lectures will be recorded for PIRSA; Zoom link will be provided to registered participants. Building Access: Participants who do not have an access card for Perimeter Institute must sign in at the security desk before each session. When attending the lectures you can use the free self-serve coffee/water station on the ground floor. Meals and snacks at the on-site Black Hole Bistro are available at the full menu cost. -
Classical Physics (Core), PHYS 776, September 3 - October 4, 2024
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 Field Theory in particular, as well as in General Relativity and Quantum Gravity. Instructor: Aldo Riello Students who are not part of the PSI MSc program should review enrollment and course format information here: https://perimeterinstitute.ca/graduate-courses -
Machine Learning 2023/24
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 starting your own machine learning projects. -
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Postdoc Welcome 2021
As COVID-19 continues to impose gathering restrictions, the “Postdoc Welcome 2021” will continue as a virtual event this year and will be hosted on Thursday, October 28 and Friday, October 29. Each new postdoc will be given 5 minutes to introduce themselves to the PI Community. The time will be used to tell us a little bit about themselves and to showcase their current research. These presentations are very casual and should not be misconstrued as formal talks. Some discussion will follow the presentations, whereby current PI Residents may have the opportunity to ask questions.
There will be two 60-minute sessions:
Thursday, October 28: 12:00 – 1:00 pm
Friday, October 29: 11:30 – 12:30 pmAll PI Residents are encouraged to attend. Registration will remain open until 9:00 am on Thursday, October 28.
Please register for the Postdoc Welcome via the event website: https://events.perimeterinstitute.ca/event/9/overview
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PSI 2019/2020 - Machine Learning for Many-Body Physics
PSI 2019/2020 - Machine Learning for Many-Body Physics