The Founder



GÖRAN GUSTAFSSON
Lectures in Mathematics


September 9–11, 2025
KTH, Stockholm

Alfio Quarteroni
Professor Emeritus, Politecnico di Milano, Milan, Italy and Ecole Polytechnique Fédérale de Lausanne, Switzerland

Poster

About the speaker

Alfio Quarteroni is an Emeritus Professor at Politecnico di Milano and at EPFL, Lausanne. He is the founder of MOX at Politecnico di Milano. Quarteroni is a member of several prestigious academies, including the Accademia Nazionale dei Lincei, the European Academy of Sciences, the Academy of Europe, the Lisbon Academy of Sciences, and the Italian Academy of Engineering and Technology. He has authored 25 books and more than 400 research papers. Quarteroni has been honored with numerous awards, including the International Galileo Galilei Prize for Sciences in 2015, the ECCOMAS Euler Medal in 2022, the ICIAM Lagrange Prize in 2023, the Blaise Pascal Prize for Mathematics in 2024, the ECCOMAS Ritz-Galerkin medal in 2024, the SIAM Ralph Kleinman Prize in 2025.

Lectures

First Lecture:
Mathematical Modeling and Numerical Simulation of Heart Function

Abstract: Computational medicine serves as a powerful engine for mathematical innovation, posing complex challenges that drive the development of new numerical methods and deepen our understanding of human physiology. At the same time, it offers critical support to clinicians by enabling more accurate diagnoses, tailored therapies, and personalized surgical planning. This lecture will focus on the multiphysics and multiscale complexities inherent in the mathematical model of the cardiac function, including the impact of data uncertainty, patient variability, and the high-dimensional nature of these systems. We will present the iHEART simulator—a comprehensive model of the human heart that integrates physics-based modeling with data-driven techniques—demonstrating how this hybrid approach can effectively address these challenges and contribute to both scientific insight and clinical practice.

Second and Third Lectures:
Physics-Informed and Data-Driven Models for Solving Partial Differential Equations

Abstract: Recent advances in artificial intelligence have produced impressive results across a wide range of applications, yet significant concerns remain regarding accuracy, uncertainty quantification, and the opacity of AI models—often criticized as "black boxes." Scientific Machine Learning (SciML) emerges as a compelling paradigm by combining data-driven methods with models grounded in physical laws, thus fostering a transparent and interpretable framework that bridges AI and traditional scientific approaches. In the first of these two lectures, we will delve into the mathematical foundations of machine learning, examining core algorithms, theoretical properties, and their limitations. The following lecture will be dedicated to Scientific Machine Learning, with a particular focus on operator learning strategies for the numerical resolution of partial differential equations. This approach demonstrates how physical constraints and data can be harmoniously integrated to enhance the reliability and performance of numerical solvers.

Date and location

Lecture 1:

Monday, September 9, 3.15–4.15 pm
Room K1 (directions)

 

Lecture 2:

Tuesday, September 10, 3.15–4.15 pm
Room K1 (directions)

 

Lecture 3:

Wednesday, September 11, 3.15–4.15 pm
Room K1 (directions)

 


Sponsored by the Göran Gustafsson Foundation