CBMS Conference: Deep Learning and Numerical Partial Differential Equations

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This award provides support for the Conference Board of the Mathematical Sciences conference "Deep Learning and Numerical PDEs," to be held at Morgan State University in Baltimore, June 19-24, 2023. This conference will bring together experts from mathematics, computer science, and engineering on topics involving deep learning and numerical partial differential equations (PDEs). As such, it will provide an education platform and forum for experts and young researchers to communicate new advancements in theoretical and application developments. The interaction between theory and application experts is intended to spark new ideas and collaborative projects. The conference will fund approximately 30 participants, especially graduate students, postdoctoral associates, early-career researchers, and individuals from underrepresented groups. The meeting aims to provide extraordinary opportunities to the participants to study the fundamental mathematical theory of deep learning, communicate potential important research directions, discuss possible practical applications, and interact with leading researchers. The distinguished principal lecturer will be Verne M. Willaman Professor Jinchao Xu of Pennsylvania State University. The lectures will present the latest developments on the theory and applications of deep learning, bridging models and algorithms from two different fields: (1) machine learning, including logistic regression and deep neural networks, and (2) numerical PDEs, including finite element and multigrid methods. The lecture series builds upon a discussion on the latest developments in machine learning models and algorithms and presents cutting-edge research on intrinsically connected topics to the participants of the conference. As a result, this conference is expected to bring novel insights into the understanding of deep learning, and to further promote their analysis and applications in different scientific and engineering fields. For more information, please refer to the conference webpage: https://sites.google.com/view/nsf-cbms-dl-nmpde/homeThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.