Statistical and Computational Challenges in Probabilistic Scientific Machine Learning (SciML)

Description

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Scientific Machine Learning (SciML) holds immense promise in complementing and enhancing classical methods, thus facilitating scientific discovery and revolutionizing engineering practices. Particularly note-worthy is its probabilistic viewpoint, which offers novel strategies for comprehending and addressing challenges posed by model uncertainties in real-world applications. The workshop aims to serve as a platform for researchers across diverse scientific domains to exchange scientific and technical expertise in related topics. It provides an opportunity to confront statistical and computational challenges collectively while fostering communication channels to bridge the scientific computing and machine learning communities.

Organizers

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L D
Laurent Demanet Massachusetts Institute of Technology (MIT)
Q L
Qin Li University of Wisconsin-Madison
R W
Rebecca Willett University of Chicago
L Z
Leonardo Zepeda-Núñez Google and University of Wisconsin-Madison

Registration

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