Speaker: Prof. Dr. Philipp Grohs
Moderation: Prof. Dr. Gitta Kutyniok (LMU)
In a recent effort to push modern tools from machine learning into several areas of science and engineering, deep learning based methods have emerged as a promising alternative to classical numerical schemes for solving problems in the computational sciences – example applications include fluid dynamics, computational finance, or computational chemistry. Philipp Grohs illuminates the limitations and opportunities of this approach, both on a mathematical and an empirical level.
Philipp Grohs is Professor of Mathematical Data Science at the University of Vienna and currently Visiting Fellow at CAS.
The Center for Advanced Studies at LMU provides a forum for scientific exchange and discussion that bridges the divide between the established disciplines. Its activities are designed to promote all forms of collaborative research and to stimulate interdisciplinary communication within the University. In addition, it facilitates the integration of visiting scholars and scientists into the academic life of the University.