22.05.23, 12:00 Uhr

München • 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.
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