Deep Learning in Scientific Computing (2023)
Description
Machine Learning, particularly deep learning is being increasingly applied to perform, enhance and accelerate computer simulations of models in science and engineering. This course aims to present a highly topical selection of themes in the general area of deep learning in scientific computing, with an emphasis on the application of deep learning algorithms for systems, modeled by PDEs.
Learning objectives
- Aware of advanced applications of deep learning in scientific computing
- Familiar with the design, implementation, and theory of these algorithms
- Understand the pros/cons of using deep learning
- Understand key scientific machine learning concepts and themes
Lecturers
Deputy head of Seminar for Applied Mathematics
Seminar für Angewandte Mathematik
Rämistrasse 101
8092
Zürich
Switzerland
ETH AI Center
Andreasstrasse 5
8092
Zürich
Switzerland
Teaching Assistants
Seminar für Angewandte Mathematik
Rämistrasse 101
8092
Zürich
Switzerland