WELCOME TO THE SOUTH AFRICAN THEORY AND COMPUTATIONAL SCHOOL (SATACS)
We’re a decentralised, semivirtual, national teaching programme in theoretical physics and computational sciences. Our goal is to provide teaching of the highest quality and at a level similar to that found in elite postgraduate programmes around the world. A critically important aim is to provide a pathway to this worldleading material for students who are registered at a South African university, with a particular emphasis on transformation. Crucially, we want to build and promote a South African community of students who will work together, learn together, and support each other through inperson and, especially, virtual platforms.
We intend for the knowledge presented in these courses to broaden and deepen the base of knowledge for students to then use to perform research of the highest quality and have a background that allows engagement with the elite research and researchers of the world.
Please note:
 Individual courses will be run if there is sufficient student interest.
 Students may only apply for / attend the SATACS courses if they are registered at a South African university.
To learn more about the individual courses presented in 2021, please click here.
Courses to be presented in 2022:
Machine Learning for Theoretical Physicists
Outline:

 Intro to machine learning
 Statistical theory and naive bases
 Regression tests and training ML models
 Gradient free optimisation methods
 Classification tasks and model evaluation
 Ensemble modeling
 Unsupervised learning: Clustering
 Unsupervised learning: Dimensionality Reduction
 Reinforcement Learning
 Intro to Neural Networks
 Pytorch
 Convolutional Neural Networks
 Recurrent Neural Networks
PLUS… a project based on the above topics on a research area of the student’s choice
Skills outcome:
At the end of the course, students will have an ability to choose and implement modern machine learning techniques using Python with appropriate training/validation and testing pipelines, hyperparameter tuning etc.
Prerequisites:
Python, including familiarity with object oriented coding + calculus and linear algebra.
Dates:
 Semester 1: 16 August – 11 November 2022
 Semester 2: January – March 2023 (dates to be confirmed).
Lesson format:
Synchronous and asynchronous video lecture delivery.
Method of evaluation:
Coding problems throughout applied to unseen datasets and a project.
Lecturer biography:
Jonathan Shock is an Associate Professor at the University of Cape Town. He has a PhD in theoretical physics from the University of Southampton, focusing on string theory, and continues to work in this field along with researching in machine learning, neuroscience and medical data analysis.
E: jon.shock @ gmail.com  W: www.shocklab.net  Publications
Differential Geometry
1. Preliminaries
 Maps
 Vector spaces and linear algebra
 Multilinear algebra and tensors
 Topological spaces
 Neighbourhoods and Hausdorff spaces
2. Differentiable Manifold
 Differentiable manifold
 Calculus on manifolds
 Flows and Lie Derivatives
3. Affine Manifolds
 Parallel transport
 Affine Connection
 Covariant derivatives
 Curvature and Torsion
4. Riemannian Geometry
 Riemann and pseudoRiemann manifolds
 Metric connection
 LeviCivita connection
 Applications
5. Symmetries
 Lie group and Lie Algebra
 Action of Lie groups on Manifolds
 Isometries and Conformal Transformations
 Killing and Conformal Killing Vector Fields
6. Differential Forms and Exterior Calculus: TBD, if time permits
Prerequisites:
Linear Algebra, Multivariable Calculus.
Dates:
14 February – 19 May 2022
Lesson format:
Synchronous lectures with a mixture of facetoface and synchronous tutorial/discussion sessions.
Method of evaluation:
Problem sets, project and a final exam.
Lecturer biography:
Shajid Haque received his PhD in Theoretical Physics from UWMadison in 2011. He did postdoctoral research at Wits University from 2012 to 2015 and at UCT from 2015 to 2017. He is interested in the applications of quantum information theory in quantum many body system, quantum field theory, holography and cosmology.
E: shajid.haque @ uct.ac.za  Publications
Introduction to General Relativity
Outline:
 Lie groups and symmetries
 Review of special relativity
 Tensor calculus
 Differential forms
 Review of differential geometry
 Geodesics
 Curvature and the Riemann tensor
 Killing vectors
 Maximally symmetric spaces
 Einstein’s equations
 The EinsteinHilbert action
 Spherically symmetric solutions
 Basics of black hole physics.
Skills outcome:
At the end of the course the student is expected to:
 show familiarity with the basic tools of GR such as the use of tensors, the metric, the meaning of curvature and the various curvature tensors.
 be able to study the motion of free particles on curved spaces by use of the geodesic equation.
 understand the mathematical description of symmetry via the concept of Killing vectors
 reflect on the difficulty of solving Einstein’s equations and provide examples of techniques for doing so.
 discuss the main features of static black holes such as event horizons, and (qualitatively) the notion of temperature and entropy for black holes and the paradoxes it leads to.
Prerequisites:
Students are encouraged to have a background in differential geometry (e.g. from the SATACS Differential Geometry course).
Special relativity, familiarity with coordinate systems for Euclidean space (Cartesian, spherical etc). Some of the exercises will require symbolic manipulation software, preferably SageMath, but the choice is up to the student.
Dates:
2nd quarter, 4 April – 20 May, 2022; asynchronous recorded lectures with two weekly live tutorial/discussion sessions.
Method of evaluation:
Weekly assignments and a final project.
Lecturer biography:
Konstantinos Zoubos is Associate Professor at the Physics Department of the University of Pretoria. His research interests are in supersymmetric Quantum Field Theory and String Theory, with an emphasis on integrable structures and the tools to analyse them, such as quantum groups.
E: konstantinos.zoubos @ up.ac.za  W: Lecturer’s personal website
Introduction to Cosmology
Outline:
 The Cosmological Principle
 Cosmological Models
 Inflationary Cosmology
 Cosmic Acceleration
 Largescale Structure Formation
 [Some Advanced Topics] Beyond Standard Cosmology.
Skills outcome:
The course will offer hot topics in modern cosmology.
At the end of the course, the students are expected to:
 Understand the assumptions in cosmology that led to the formulation of the standard cosmological model
 Derive the cosmological field equations and analyse their solutions
 Demonstrate length and time scales of the universe
 Apply the specialized and integrated knowledge of general relativity and cosmology to critically analyse the shortcomings of the Big Bang Model, and the need to introduce inflation, dark energy, and dark matter
 Understand the physical processes and mechanisms that lead to largescale structure formation
 Critically analyse the standard cosmological model and understand the need to look at new paradigms beyond the standard model.
Prerequisites:
Introduction to General Relativity.
Dates:
1 semester, 1 August — 4 November, 2022; weekly synchronous lecture videos and tutorials.
Method of evaluation:
Weekly assignments, projects and an exitassessment exam.
Lecturer biography:
Amare Abebe received his PhD in cosmology from the University of Cape Town in 2013. He held a postdoc position at the NorthWest University from 2014 to 2015 after which he joined the faculty at this same institution. He is currently a Professor of Physics and his broad research interests lie in gravitation and cosmology.
E: amare.abbebe @ gmail.com  W: Lecturer’s personal website  Publications
Extreme Gravity or An Introduction to Black Holes and Gravitational Waves
Outline:
 The Schwarzschild solution
 Black Holes
 Penrose Diagrams
 Gravitational waves
Skills outcome:
These are key modern areas of current research in cosmology/astrophysics and gravity and yet the maths and physics underlying these areas is quite old. Nonetheless, the maths and physics aren’t taught at most places in the country, if anywhere. This course will prepare students to enter these fields if they want to – by the end of the course, students will be beautifully trained for the future. And should they not choose these fields, the work will skill them for other related fields.
Prerequisites:
Introduction to General Relativity.
Dates:
25 July — 4 November 2022; in person and synchronous lecture videos.
Method of evaluation:
Weekly assignments, a project and an exam.
Lecturer biography:
Amanda Weltman received her PhD in Physics from Columbia University in 2007. She held a postdoctoral researcher position at the University of Cambridge from 20072009 before joining the University of Cape Town as a Senior Lecturer. She is currently a Professor at UCT with research interests in astrophysics, fundamental physics, cosmology, and gravity.
E: amanda.weltman @ uct.ac.za  W: Lecturer’s personal website  Publications
Introduction to PrivacyPreserving Schemes for Applications in Artificial Intelligence
Outline:
The course will provide an introduction to various privacy tools that can be used in order to enable privacypreserving Artificial Intelligence (with focus on Machine Learning and Natural Language Processing subfields of Artificial Intelligence).
Skills outcome:
The developments in Data Science bring with them the challenge of dealing with sensitive datasets; where the need to preserve the privacy of the participants in the datasets is of utmost importance. This challenge warrants the need to explore privacypreserving schemes that can be used in order to ensure the privacy of the participants in the datasets. This course is intended to introduce these privacypreserving schemes to the students, and demonstrate how such schemes can be applied in Machine Learning and Natural Language Processing subfields of Artificial Intelligence.
At the end of the course, students would have acquired the skills on implementing Data Privacy tools such as Differential Privacy and Federated Learning in Artificial Intelligence tasks. These acquired skills would then make it possible for students to appropriately deal with sensitive datasets such as healthcare datasets, in order to design privacypreserving Artificial Intelligence techniques.
Prerequisites:
Python programming basics, Linear Algebra, Calculus, and Probability Theory.
Dates:
1 semester, 4 April — 24 June, 2022; synchronous lecture videos.
Method of evaluation:
Weekly problem sets and an exam.
Lecturer biography:
Makhamisa Senekane is a Senior Researcher in the Institute for Intelligent Systems at the University of Johannesburg. Prior to that, he worked as a Lecturer in the Department of Physics and Electronics at the National University of Lesotho, as a Senior Lecturer in the Faculty of Information and Communication Technology at Limkwokwing University of Creative Technology (Lesotho), and as a Lecturer in the Faculty of Computing at Botho University (Maseru Campus). He has obtained his PhD in Physics from the University of KwaZuluNatal, his MSc.Eng in Electrical Engineering from the University of Cape Town, and his B.Eng in Electronics Engineering from the National University of Lesotho. His research interests include data science, data security, data privacy, artificial intelligence (machine learning and natural language processing), and quantum information processing (quantum cryptography, quantum computing, and quantum machine learning).
Mathematical Structures
Outline:
The course will present a survey of and interconnections between various mathematical structures that arise on the overlap of abstract mathematics, physics, and computer science. Just to give an example: Hilbert spaces arise in quantum mechanics, their subspaces lead one to quantum logic, while the structure of subspaces of a Hilbert space is that of a “lattice” from order theory in abstract mathematics, which is a generalization of a Boolean algebra, relevant to computer science. The methodology for comparing mathematical structures in this course will rely on ideas from set theory, which enable description of mathematical structures via “elements” (or “points”), and category theory, which looks at networks of mathematical structured formed by structure preserving functions between them (e.g., linear maps in the case of vector spaces).
Skills outcome:
The goal of the course is for the students to see the bigger picture behind modern abstract mathematics and its links to other sciences, while at the same time acquiring research skills in mathematics (thanks to the problemsolving based nature of the course). The students will leave with an intuition for “how abstract mathematics works”. They will also be able to see how the mathematics that they knew previously fits into a broader realm of mathematical structures. Such course is not offered anywhere in South Africa. It will be an excellent complement to existing specialized courses in universities.
Dates:
1 August — 4 November 2022; synchronous lectures over Zoom.
Method of evaluation:
Group work assignments. Each group work will consist of problems that the students must work together on. Marks will be awarded per group. Some of the group work will be to record a joint video presentation explaining their solutions to given problems.
Lecturers:
This course will be presented by a team of investigators in a 2022 NITheCS research programme in Mathematical Structures. Course convenors are the Principal Investigators (listed alphabetically): Partha Pratim Ghosh (UNISA), Yorick Hardy (WITS), Zurab Janelidze (SU), and Cerene Rathilal (UJ).
E: zurab @ sun.ac.za  W: Lecturer’s personal website  Publications
Advanced Methods for Mathematical Physics
Outline:
 Winding number in complex analysis and the generalised Cauchy theorem
 Integral representations and differential equations
 Advanced method for Green’s functions – resolvents and operator methods
 Spectral analysis in quantum mechanics
 Integral equations
Skills outcome:
The course is designed as one in advanced mathematical methods, aimed at theoretical/mathematical physics students. Material is drawn from various sources, including Arfken & Weber and Morse & Feshbach. Students will leave the course with a working vocabulary in complex analysis, spectral analysis, and some more mathematical aspects of quantum mechanics. These topics are foundational for a number of topics in theoretical and mathematical physics across the spectrum of the South African theory community.
Prerequisites:
Basic knowledge of complex analysis (contour integrals, Cauchy’s theorem etc), differential equations (mostly ordinary); some quantum mechanics is useful but not essential.
Dates:
25 July — 4 November 2022; meeting twice a week for 2 hours per lecture.
Method of evaluation:
Weekly problem sets.
Lecturer biography:
Jeff Murugan is Professor of Mathematical Physics and head of the Laboratory for Quantum Gravity & Strings at the University of Cape Town. He received a PhD in Noncommutative Geometry in String Theory from UCT and Oxford in 2004. He has held a postdoctoral position at Brown University from 20042006 and was a member at the Institute for Advanced Study in Princeton in 20162017. His research interests lie primarily in understanding emergent phenomena, from condensed matter to neurophysics. His recent focus has been on lowdimensional quantum field theory, topological quantum matter and quantum chaos in disordered systems.
E: jeffmurugan @ gmail.com  W: Lecturer’s personal website  Publications
Nonlinear Hamiltonian Dynamics and Chaos
uOutline:
Nonlinear Hamiltonian dynamics is used to study the behavior of systems coming from a wide variety of scientific fields, the most important of them being classical mechanics, astronomy, optics, electromagnetism, solid state physics, quantum mechanics, and statistical mechanics. An important phenomenon appearing in nonlinear systems is chaos, which is attributed to the sensitive dependence of a system’s dynamical evolution on its initial conditions.
In this course we will implement several modern numerical techniques to investigate and quantify the chaotic behavior of lowdimensional Hamiltonian systems and area preserving symplectic maps. In particular, we will discuss the following topics:
 Chaos
 Autonomous Hamiltonian systems and symplectic mappings
 Numerical integration of Hamilton equations of motion
 Poincaré surface of section
 Integrals of motion
 Symplectic integrators
 Variational equations
 Tangent Map Method
 Maximum Lyapunov exponent
 Spectrum of Lyapunov exponents
 Chaos indicators
Skills outcome:
Students will learn basic concepts of nonlinear dynamics (particularly in the framework of Hamiltonian mechanics), will be introduced into various traditional as well as modern techniques of analysing chaotic systems and will also implement these methods by themselves in studying some prototypical dynamical models. Thus, students will acquire and practice some basic tools of nonlinear dynamics, which can be implemented in a wide spectrum of problems coming from various scientific fields
Prerequisites:
Basic knowledge of 2nd year classical mechanics.
Dates:
1 semester, 1 August — 4 November, 2022; synchronous lecture videos.
Method of evaluation:
Students will be asked to do 4 assignments of the form of implementing some numerical methods to a particular dynamical system. Then they will have to submit for evaluation at predefined dates their report for each assignment. Depending on practical constrains they might be requested to orally present their assignment (brief presentation of at most 20 minutes). At the beginning of week 6 each student will get a final project concerning the theory and application of a modern chaos technique which he/she will have to finalize by the end of the course, similarly to the 4 assignments. This assignment will be more demanding as it will also require some bibliographical research from the students along with the more applied aspect of the project (numerical application of the chaos indicators to some dynamical systems).
The course’s final mark (FM) will be defined at a 50% level by your mean mark of your 4 assignments (AS1, AS2, AS3, AS4) and 50% by your final project (FP) mark: FM=(AS1+AS2+AS3+AS4)/4 +FP/2.
Lecturer biography:
Charalampos (Haris) Skokos received his PhD in Nonlinear Dynamical Systems from the University of Athens, Greece. After a number of prestigious appointments, Prof Skokos joined the faculty of the University of Cape Town in 2013; he is now Associate Professor of Mathematics and Applied Mathematics and Deputy Head of Department. His research interests are in nonlinear dynamical systems, chaotic dynamics and Hamiltonian systems.
E: haris.skokos @ uct.ac.za  W: Lecturer’s personal website  Publications
Quantum Field Theory I
Outline:
 Postulates of QM and SR
 Quantizing the free scalar field
 Interpreting the results
 Connecting to experiments; in and out states; LSZ reduction
 LehmanKallen representation; GellMann–Low theorem; cross sections
 Feynman rules for scalar fields
 Introduction to QED, QED Feynman rules, and trace technology for cross sections
Skills outcome:
Students will leave the course with a deep understanding of 1) free scalar quantum field theory and 2) Feynman calculus for computing cross sections involving scalar particles. Students should also have a good facility for computing Feynman diagrams and cross sections related to QED processes.
Prerequisites:
A course in advanced quantum mechanics and a course in which special relativity was treated in some detail.
Dates:
25 July — 2 September 2022; synchronous lecture videos twice a week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications
Quantum Field Theory II
Outline:
 Brief introduction to group theory and representations and their importance in quantum state space and constraining potential Lagrangians
 Nonrelativistic quantum rotations and spin
 Irreducible representations of the Lorentz group SO(3,1)
 Free 2D Weyl spinor fields
 Interacting 2D Weyl spinor fields
 4D Majorana and Dirac fields
 Free spin1 gauge fields. BRST gauge fixing. Nonabelian gauge theory
 Spinor helicity techniques. BCFW recursion.
Skills outcome:
Students should have a thorough understanding of quantum field theories for particles up to spin1.
Prerequisites:
Quantum Field Theory I.
Dates:
One quarter: 12 September — 21 October 2022; synchronous lecture videos twice per week, one synchronous tutorial per week.
Method of evaluation:
Weekly problem sets and a project.
Lecturer biography:
Associate Professor W. A. Horowitz received his PhD in Physics from Columbia University in 2008. He held a postdoctoral research position at the Ohio State University from 2008 to 2010, and then joined the faculty at the University of Cape Town. Prof Horowitz is an expert in the use of perturbative quantum field theory and AdS/CFT methods in phenomenological highenergy quantum chromodynamics applications.
E: wa.horowitz @ gmail.com  W: Lecturer’s personal website  Publications