Data-Driven Mathematical Modelling

Data is displayed for academic year: 2023./2024.

Lecturers

Assoc. Prof.
Dario Bojanjac

Course Description

In order to better understand and predict experiment outcomes, we describe the real world using mathematical models. A mathematical model is a description of a system or phenomenon using mathematical language, and the process of developing a mathematical model is called mathematical modeling. Since the real world is too complex to model its parts or phenomena in their original form, when developing a mathematical model, we choose which properties of the real world to take into account and which to ignore. This decision is based on experience, and with the advancement of technology, it is increasingly based on measured data. The goal of the course Data-Driven Mathematical Modeling is to understand data in the context of mathematical modeling and to develop methods and techniques that extract parameters of the model or entire models based on the data.

Study Programmes

Postgraduate doctoral study programme

Literature

Simona Cocco, Rémi Monasson, Francesco Zamponi (2022.), From Statistical Physics to Data-Driven Modelling, Oxford University Press
Bruno Després (2022.), Neural Networks and Numerical Analysis, Walter de Gruyter GmbH & Co KG
Steven L. Brunton, J. Nathan Kutz (2022.), Data-Driven Science and Engineering, Cambridge University Press
Akinori Tanaka, Akio Tomiya, Koji Hashimoto (2021.), Deep Learning and Physics, Springer Nature

For students

General

ID 254087
  Winter semester
6 ECTS
L0 English Level