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COS 522: Computational Methods for Quantum Field Theories

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COS 522: Computational Methods for Quantum Field Theories

Course Title	Computational Methods for Quantum Field Theories
Course Code	COS 522
Course Type	Elective
Level	PhD
Instructor’s Name	TBA (Lead Instructor), Constantia Alexandrou
ECTS	5
Lectures / week	1 (90 min. each)
Laboratories / week	1 (90 min. each)
Course Purpose and Objectives	The purpose of the course is to equip students with the necessary skills for carrying out research involving simulations of quantum field theories. The course will teach PhD students to use state-of-the-art computational methods for simulating and studying quantum field theories. Lectures introducing the methodologies will be followed by hands-on practical training, requiring students to develop their own codes, obtaining data from the simulations, and performing statistical analysis.
Learning Outcomes	By completing the course, students will have learned and applied techniques as used currently in state-of-the-art simulations of quantum field theories. These include: - Simulation techniques for quantum field theories, such as heat-bath, multilevel approached, and Hybrid Monte Carlo - Scaling and optimizing application codes of multi-dimensional lattices and using such software on leadership computers equipped with novel computer architectures - Management and analysis of large structured data sets
Prerequisites	None
Background Requirements	- Knowledge of C/C++ and MPI for simulation codes - Knowledge of Python for data analysis - Background in quantum mechanics
Course Content	Week 1-2: Path integral formulation for quantum mechanical systems and gauge theories. First computer implementations and best practices for optimized codes. Week 3-5: Monte Carlo methods for relativistic quantum field theories and gauge theories; Applications to the scalar field theories and pure gauge theory. Week 6-7: Parallelization strategies, scalability, and implementation details for multi-dimensional lattices and for different architectures (CPUs and GPUs)
Teaching Methodology	- One 3-hour session per week including a lecture (c.a. 1.5 hours) and handson practical exercises applying the taught content (c.a. 1.5 hours) - After each session, students are expected to complete their practical exercise with remote assistance by lecturer - Students will be required to deliver two homework exercises, that will be requested after the third and fifth weeks - A final project, report, and presentation by each student
Bibliography	- Lecture notes - Code samples provided by lecturer - Papers
Assessment	- Coursework - Final project
Language	English