Scientific Machine Learning: Theory and Algorithms

February 21, 2024 - February 23, 2024

Organizers:

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Maria Cameron
University of Maryland
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Chunmei Wang
University of Florida
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Haizhao Yang
University of Maryland

Machine learning has recently transformed the field of scientific computing, resulting in scientific machine learning as an emerging field. Numerical algorithms for solving partial differential equations (PDEs) based on ideas and techniques adapted from artificial intelligence (AI) and data science tremendously broadened the spectrum of applications where this task became feasible in high dimensions and offered new opportunities for traditional applications. Data-driven numerical solvers for inverse problems have become application-dependent with optimized computational efficiency and accuracy. Physics-informed machine learning has contributed to new methods for domain-aware and interpretable modeling, simulation, and discovery from scientific data. AI-aided algorithms have made the acceleration of numerical computation automatic and domain-aware. At the same time, there are many questions that are yet to be explored and answered related to error control, scalability, robustness, and the curse of dimensionality in large-scale and high-dimensional systems. The goal of this workshop is to bring together pioneering scientists who advance theory and algorithms for scientific machine learning with the purpose of sharing ideas, brainstorming challenging problems, and identifying new targets.