Associate Professor

Department of Mathematical Sciences

Durham University

office: MCS 3080

mailkasper.peeters@durham.ac.uk
phone+44-191-3343113

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my public github repositories

Symbolic Computer Algebra is used in many areas of science, and various systems exist. My own open-source system “Cadabra” is a symbolic computer algebra system designed specifically for the solution of problems encountered in field theory. It has extensive functionality for tensor computer algebra, tensor polynomial simplification including multi-term symmetries, fermions and anti-commuting variables, Clifford algebras and Fierz transformations, component computations, implicit coordinate dependence, multiple index types and many more.

Ideas about a duality between gauge fields and strings have been around for many decades. Around the end of the last century, these ideas took a much more concrete mathematical form (in the form of “holographic duals” involving strings in extra dimensions). String descriptions of the strongly coupled dynamics of semi-realistic gauge theories, exhibiting confinement and chiral symmetry breaking, are now available. These provide remarkably simple ways to compute properties of the strongly coupled “quark-gluon fluid” phase, and also shed new light on various phenomenological models of hadron fragmentation.

Together with a group of people at the Department of Mathematical Sciences and the Biophysical Sciences Institute, I have worked on applications of mathematics to biological systems. Recent work in this area includes the analysis of dynamics of the protein capsids of icosahedrical viruses, such as the one depicted here, and work on microtubule assembly and disassembly.

I am currently (2023/2024 and next year) teaching:

Other modules I have taught in the past (click through for lecture notes):- Mathematical Modelling II
- Advanced Quantum Theory IV

- Biophysics I
- Group Theory
- Euclidean Field Theory

Projects I have supervised over the years:

- Interstellar
- Desktop Black Holes
- Face Recognition
- Physics Informed Neural Networks
- ...