Abstract: While artificial intelligence has achieved remarkable advances in recent years, it still suffers from inaccuracies, uncertainties, and a lack of transparency—often earning the label of a "black box" technology. Scientific machine learning offers a promising solution by integrating data-driven algorithms with digital models rooted in physical principles. This powerful combination fosters a meaningful synergy between artificial intelligence and human expertise, grounded in natural laws and rigorous scientific frameworks. In this presentation, I will illustrate these concepts through a practical application: the development of a mathematical simulator that accurately replicates cardiac function.
Bio: Prof. Alfio Quarteroni is Professor Emeritus at the Politecnico of Milan (Italy), and Professor Emeritus at the EPFL (Swiss Federal Institute of Technology). He has been the director of the Chair of Modelling and Scientific Computing at the EPFL (Swiss Federal Institute of Technology), Lausanne (Switzerland), from 1998 until 2017. He is the founder (and first director) of MOX at Politecnico of Milan (2002) and MATHICSE at EPFL, Lausanne (2010). He is co-founder (and President) of MOXOFF, a spin-off company at Politecnico of Milan (2010).
He is member of the Italian Academy of Science (Accademia Nazionale dei Lincei), the European Academy of Science, the Academia Europaea(Academy of Europe), the Lisbon Academy of Sciences, the Istituto Lombardo Accademia di Scienze e Lettere and the Italian Academy of Engineering and Technology.
He is author of 26 books (some of them translated into up to 7 languages), editor of 8 books, author of about 400 papers published in international Scientific Journals and Conference Proceedings, member of the editorial board of 25 International Journals, and Editor in Chief of two-book series published by Springer.
