Scientific Machine Learning, emerging topics
128-129
SISSA, International School for Advanced Studies, main campus, Trieste, Italy
"Scientific Machine Learning, emerging topics" is an international conference focused on the study of mathematical theory and algorithms of machine learning, and applications of machine learning in scientific computing and engineering disciplines. Particular emphasis will be on
- optimization,
- physics-informed learning,
- graph neural networks,
- neural operators,
- transformers,
- neural odes,
- generative models.
Conference fees
| Early Bird Fee | Normal Fee | |
| Students | 250€ | 300€ |
| Non students | 350€ | 400€ |
The conference fee includes: lunches (Wed. and Thu.), coffee breaks, aperitivo, conference dinner and transportation from Trieste City Center to SISSA Campus.
Early Bird Fee until May, the 31st, 2024.
Registration is closed.
Payment instructions are available at this link.
SISSA staff will then send the invoices for the payment.
Abstract submission is now closed!
A connection to SISSA campus will be guaranteed by shuttle bus with the following from Piazza Oberdan and SISSA campus:
June 18: 1:00pm (Oberdan) 6:30pm (SISSA)
June 19: 8:45am (Oberdan) 7:30pm (SISSA)
June 20: 8:45am (Oberdan) 6:30pm (SISSA)
June 21: 8:45am (Oberdan) 1:10pm (SISSA)
Moreover, SISSA campus can be reached by local public transportation: the bus 38 connects the city center to the campus every 20 minutes, but passengers need a ticket.
Talks
Keynote talks are supposed to be 1h long (45' talk + 15' questions), other talks are supposed to last 30 minutes (25' talk + 5' questions).
Poster session details
The poster session will be on Wednesday from 17:00 until 19:00. The poster holder are portrait B0 format. During the poster session, an aperitivo will be served.
Conference dinner
The conference dinner will take place at Savoy Restaurant, Riva del Mandracchio, 4, 34124 Trieste, TS on Thursday at 20:00.
Image taken from the paper by Michael M. Bronstein, Joan Bruna, Taco Cohen and Petar Veličković "Geometric Deep Learning: Grids, Groups, Graphs, Geodesics, and Gauges", arXiv preprint arXiv:2104.13478
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