Fall 2019 Modern Methods in Theoretical Physics

REGISTRATION CLOSED

09Sept2019 16Dec2019 11:00

Course

This Fall's Delta ITP Course Advanced Topics in Theoretical Physics is built around the general theme of Modern Methods in Theoretical Physics. The course is divided into three 5-week modules, which will cover the Renormalization Group, Beyond LCDM and Machine Learning. This course is aimed at advanced Master’s students, PhD and postdoctoral researchers.

Each module consists of four lectures and exercise sessions. Lectures will take place on Mondays at 11:15 - 13:00, followed by a study/exercise session from 13:45 - end. At the end of the module there is an exam. All exams are pass/fail, and you need to pass all three exams to receive credit for the course.

The location of this course rotates between the three institutes. The first module is in Utrecht. Directions to the institutes can be found here: 

Utrecht

Lectures and exams will take place in BOL - the building at Bolognalaan 101, Uithof, Utrecht. Lectureroom is BOL 1.051. >> GoogleMaps Directions by car, train etc at https://www.uu.nl/en/bolognalaan-101

Leiden

Lecture rooms in Leiden will be announced asap. 

Amsterdam

Lectures and exams will take place at Nikhef, Science Park 105, Amsterdam. Room: H331. >>GoogleMaps  

Travel reimbursement

Students who do not have an OV-card from the Dutch government can have their travel costs reimbursed from D-ITP. Please contact the local coordinator (below) for details.

Registration

Since the course already started, registration is closed for those aiming to receive credits (all three exams need to be passed).

You can however still register for seperate modules 2 and/or 3 - no credits given - by filling in the form below. Even if you do not plan to take the course for credit, please register. We cannot send important notices if you do not register ahead of time

Schedule

Module 1: Renormalization Group Methods - Lars Fritz (Utrecht)

Lectures and exercises: Sept, 9, 16, 23, 30: room BOL 1.051

Exam: Oct 7: room BOL 1.051

Abstract: In this course we cover renormalization group from the point of view of statistical physics and critical phenomena. We start with a recap of Landau theory, its victories, and its shortcomings in quantitatively accounting for critical exponents. We proceed to the Block spin renormalization as introduced by Kadanoff and finish with a discussion of renormalization group in the sense of Wilson, which cures the shortcomings of Landau theory.  The goal of the course is to become familiarized with renormalization group in the way it is usually used in statistical and condensed matter physics. If time permits there will also be a short discussion on the relation between renormalization group schemes and philosophies as used in high and low energy physics. 

Module 2: Beyond LCDM - Alessandra Silvestri (Leiden)

Lectures and exercises: Oct 14, 21, 28, Nov 4: room TBA

Exam: Nov 11; room TBA

Abstract: In this mini-course, I will give an overview of some of the outstanding open questions of Modern Cosmology. I will touch upon some challenges for the standard model of Cosmology, LCDM, and provide a comprehensive overview of the landscape of theories that go beyond LCDM, with focus on the phenomenon of cosmic acceleration. I will present in details the methodology to build such theories as well as their phenomenology on cosmological scales and our future prospects for constraining them. 

Module 3: Machine Learning: a New Toolbox for Theoretical Physics - Juan Rojo (Amsterdam)

Lectures and exercises: Nov 18, 25, Dec 2, 9; Nikhef room H331

Exam: Dec 16; Nikhef room H331

Abstract: Under the generic umbrella denomination of Machine Learning (or Artificial Intelligence) one finds a number of statistical algorithms with the common feature that they manage to solve a specific task by learning by example from large datasets, as opposed to following a set of predefined rules. In recent years, Machine Learning tools have demonstrated their impressive potential to tackle a range of challenging problems related to classification, optimization, discrimination, inter- and extrapolation, efficient parametrizations, and pattern generalization, among several others. In this course, we present an overview of the applications of Machine Learning to various areas of theoretical physics, from particle and astroparticle physics phenomenology to condensed matter and string theory to name a few. The course will be a mixture of theoretical foundations, discussion of practical applications, and hands-on tutorials. 


Contact

Dr. Lars Fritz, 
Institute for Theoretical Physics, 
Utrecht University
, 
email: l.fritz [AT] uu.nl



Prof. Koenraad Schalm
, Instituut-Lorentz for Theoretical Physics, 
Leiden University
, email: kschalm [AT] lorentz.leidenuniv.nl



Dr. Wouter Waalewijn
, Institute for Theoretical Physics, 
University of Amsterdam
, 
email: w.j.waalewijn [AT] uva.nl



Published by  D-ITP