Neural Differential Equations and Applications

Student: Polina Abramova

Faculty: Faculty of Informatics, Mathematics, and Computer Science (HSE Nizhny Novgorod)

Educational Programme: Data Mining (Master)

Year of Graduation: 2025

The investigation of neural ordinary differential equations (ODES) in machine learning and applications is of practical importance for solving a number of fundamental problems. This work will consider some methods, in particular Bayesian and deterministic approaches used in the field of uncertainty estimation (UQ) for scientific machine learning (SciML) tasks. To identify a more efficient and reliable model, an illustrative example will be analyzed: the inverse problem for the nonlinear Korteweg-de Vries partial differential equation (PDE).

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