Applied Mathematics Theses and Dissertations

Theses and Dissertations for the Applied Mathematics department.

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Description:: Deep learning builds functions by composing layers that depend on parameters. In a deep linear network (DLN), this composition is a product of matrices. The …
Year:: 2026
Contributor:: Kotwal, Tejas (creator); Menon, Govind (Advisor); Darbon, Jérôme (Reader); Sandstede, Björn (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: gradient flows; deep linear network; Symplectic geometry; Matrix Factorization
Collection:: Applied Mathematics; Theses and Dissertations

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Description:: This thesis introduces novel neural network architectures designed to discover dynamical systems from physical data and as well as solve dynamical systems rising from optimal …
Year:: 2024
Contributor:: Zhang, Zhen (creator); Karniadakis, George (Advisor); Keith, Brendan (Reader); Darbon, Jérôme (Reader); Brown University. Department of Applied Mathematics (sponsor)
Genre:: theses
Subject:: dynamical systems; Deep Learning; Symplectic geometry
Collection:: Applied Mathematics; Theses and Dissertations

Full Record