Scientific Machine Learning (23w5129)

Organizers

(Brown University)

(University of Pennsylvania)

Zhiping Mao (Xiamen University)

(ETH Zürich)

(University of Texas at Austin)

Description

The Banff International Research Station will host the "Scientific Machine Learning" workshop in Banff from June 18 to June 23, 2023.



Machine learning is undergoing a renaissance that has revolutionized the ability to process and extract information from data. Along the way, it has created new technology and influenced almost all areas of computation, leading to game-changing advancements in language processing, advertising, cyber-security, computer vision, automated decision-making, and more.



Scientific computing techniques allow us to use computers to rigorously simulate, quantify, and predict environmental responses, make informed decisions, and test scientific hypotheses. Like other disciplines centered on the use of computers, scientific computing is evolving rapidly via the rise of machine learning. However, unlike many other enterprises, scientific computing requires testable explanations, predictions, reproducibility, speed, and quantification of uncertainties that push the limits of modern machine learning methods, many of which are only beginning to be rigorously understood.



This workshop aims to bring together a diverse, multi-disciplinary cross-section of pioneering researchers to foster a community, share ideas, and work together to build a lasting mathematical and social foundation. The key themes of this workshop include i) neural network design and approximability; ii) modeling, inference, prediction, and data assimilation; and iii) high-performance algorithms and scalability. The participants of this workshop possess complementary expertise in numerical analysis, approximation theory, functional analysis, probability theory, nonlinear programming, high-performance computing, computer science, statistics, engineering, and industrial applications. Putting all this knowledge together will create a synergy of effects that will profoundly impact the rigorous development of new and lasting machine learning methods for scientific computing applications.



The Banff International Research Station for Mathematical Innovation and Discovery (BIRS) is a collaborative Canada-US-Mexico venture that provides an environment for creative interaction as well as the exchange of ideas, knowledge, and methods within the Mathematical Sciences, with related disciplines and with industry. The research station is located at The Banff Centre in Alberta and is supported by Canada's Natural Science and Engineering Research Council (NSERC), the U.S. National Science Foundation (NSF), Alberta's Advanced Education and Technology, and Mexico's Consejo Nacional de Ciencia y Tecnología (CONACYT).