Publications

Order by: publication date number of citations

• Population genetics: An introduction for physicists

  Andrea Iglesias-Ramas, Samuele Pio Lipani, Rosalind J. Allen

  SciPost Phys. Lect. Notes 89 (2024) · published 10 December 2024 | Toggle abstract · pdf

  Population genetics lies at the heart of evolutionary theory. This topic forms part of many biological science curricula but is rarely taught to physics students. Since physicists are becoming increasingly interested in biological evolution, we aim to provide a brief introduction to population genetics, written for physicists. We start with two background chapters: chapter 1 provides a brief historical introduction to the topic, while chapter 2 provides some essential biological background. We begin our main content with chapter 3 which discusses the key concepts behind Darwinian natural selection and Mendelian inheritance. Chapter 4 covers the basics of how variation is maintained in populations, while chapter 5 discusses mutation and selection. In chapter 6 we discuss stochastic effects in population genetics using the Wright-Fisher model as our example, and finally we offer concluding thoughts and references to textbooks in chapter 7.

• Stochastic resetting and large deviations

  Martin R. Evans, John C. Sunil

  SciPost Phys. Lect. Notes 103 (2025) · published 16 October 2025 | Toggle abstract · pdf

  Stochastic resetting has been a subject of considerable interest within statistical physics, both as means of improving completion times of complex processes such as searches and as a paradigm for generating nonequilibrium stationary states. In these lecture notes we give a self-contained introduction to the toy model of diffusion with stochastic resetting. We also discuss large deviation properties of additive functionals of the process such as the cost of resetting. Finally, we consider the generalisation from Poissonian resetting, where the resetting process occurs with a constant rate, to non-Poissonian resetting.

• Introduction to string theory

  Carlo Maccaferri, Fabio Marino, Beniamino Valsesia

  SciPost Phys. Lect. Notes 90 (2025) · published 29 January 2025 | Toggle abstract · pdf

  These are lecture notes of the introductory String Theory course held by one of the authors for the master program of Theoretical Physics at Turin University. The world-sheet approach to String Theory is pedagogically introduced in the framework of the bosonic string and of the superstring.

• Simplified derivations for high-dimensional convex learning problems

  David G. Clark, Haim Sompolinsky

  SciPost Phys. Lect. Notes 105 (2025) · published 23 October 2025 | Toggle abstract · pdf

  Statistical-physics calculations in machine learning and theoretical neuroscience often involve lengthy derivations that obscure physical interpretation. Here, we give concise, non-replica derivations of several key results and highlight their underlying similarities. In particular, using a cavity approach, we analyze three high-dimensional learning problems: perceptron classification of points, perceptron classification of manifolds, and kernel ridge regression. These problems share a common structure—a bipartite system of interacting feature and datum variables—enabling a unified analysis. Furthermore, for perceptron-capacity problems, we identify a symmetry that allows derivation of correct capacities through a naïve method.

• Lecture notes on large deviations in non-equilibrium diffusive systems

  Bernard Derrida

  SciPost Phys. Lect. Notes 106 (2025) · published 29 October 2025 | Toggle abstract · pdf

  These notes are a written version of lectures given in the 2024 Les Houches Summer School on Large deviations and applications. They are are based on a series of works published over the last 25 years on steady properties of non-equilibrium systems in contact with several heat baths at different temperatures or several reservoirs of particles at different densities. After recalling some classical tools to study non-equilibrium steady states, such as the use of tilted matrices, the Fluctuation theorem, the determination of transport coefficients, the Einstein relations or fluctuating hydrodynamics, they describe some of the basic ideas of the macroscopic fluctuation theory allowing to determine the large deviation functions of the density and of the current of diffusive systems.

• Les Houches lectures on flow networks in biology

  Swarnavo Basu, Karen Alim

  SciPost Phys. Lect. Notes 101 (2025) · published 10 October 2025 | Toggle abstract · pdf

  Flows are essential to transport resources over large distances. As soon as diffusion becomes time-limiting, flows are needed. Flows are key for the function of multiple human organs, from the blood vasculature to the lungs, the digestive tract, the lymphatic system, and many more. While physics governs the flow dynamics, biology's response to flows governs the flow network architecture. We start with the fluid physics of Stokes flow, the prerequisite to describe the flows in biological flow networks. Then we explore how the network adaptation dynamics of biological flow networks reorganize network architecture to minimize flow dissipation or homogenize transport, storing memories of past flows along the way.

• Introduction to the usage of open data from the Large Hadron Collider for computer scientists in the context of machine learning

  Timo Saala, Matthias Schott

  SciPost Phys. Lect. Notes 96 (2025) · published 23 June 2025 | Toggle abstract · pdf

  Deep learning techniques have evolved rapidly in recent years, significantly impacting various scientific fields, including experimental particle physics. To effectively leverage the latest developments in computer science for particle physics, a strengthened collaboration between computer scientists and physicists is essential. As all machine learning techniques depend on the availability and comprehensibility of extensive data, clear data descriptions and commonly used data formats are prerequisites for successful collaboration. In this study, we converted open data from the Large Hadron Collider, recorded in the ROOT data format commonly used in high-energy physics, to pandas DataFrames, a well-known format in computer science. Additionally, we provide a brief introduction to the data's content and interpretation. This paper aims to serve as a starting point for future interdisciplinary collaborations between computer scientists and physicists, fostering closer ties and facilitating efficient knowledge exchange.