Year: 2023
CAM-Net Digest, Vol. 20 (2023), Iss. 6 : p. 4
Abstract
We are pleased to announce that registration is now open for the Maths4DL Deep Learning for Computational Physics Conference, taking place at University College London, from 4 to 6 July 2023.
Deep learning in physics represents a very active and rapidly growing field of research. This shift in approach has already brought with it many advances, which this conference aims to highlight. Recent examples include PINNs, SINDy, symbolic regression, Fourier neural operators, meta-learning, and neural ODEs to name a few. The applications also embrace many disciplines across the scientific spectrum, from medical sciences, to computer vision, to the physical sciences. We believe that the next steps for machine learning require a firm theoretical understanding and this conference will bring together like-minded individuals to discuss current and future research in this area.
Confirmed keynote speakers:
- Prof. Giovanni Alberti, University of Genoa
- Dr Steve Brunton, University of Washington
- Prof. Elena Celledoni, Norwegian University of Science and Technology (NTNU)
- Asst. Prof. Sophie Langer, University of Twente
- Dr Chris Rackauckas, Massachusetts Institute of Technology (MIT)
More information can be found on the conference webpage
https://maths4dl.com/newsevents/conference-on-deep-learning-for-computational-physics/
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Journal Article Details
Publisher Name: Global Science Press
Language: Chinese
DOI: https://doi.org/2023-CAM-21571
CAM-Net Digest, Vol. 20 (2023), Iss. 6 : p. 4
Published online: 2023-01
AMS Subject Headings: Global Science Press
Copyright: COPYRIGHT: © Global Science Press
Pages: 1