UC Berkeley

This dissertation studies the application of machine learning in the fields of Glaciology and Paleomagnetism. In the past few years, there have been significant advances in introducing physical constraints in the form of inductive biases in data-driven approaches coming from statistics and machine learning. This gave rise to the field of physics-informed machine learning, which we will introduce in Chapter 1. Chapters 3 and 4 will cover the application of neural differential equations for ice flow modelling, showcasing how the differentiable programming techniques introduced in Chapter 2 have been successfully applied for the inversion and calibration of the internal ice viscosity of mountain glaciers with different climates. This led to the development of ODINN.jl, a multilanguage Julia-Python package for the modelling of global glacier-climate interactions. We will finalize our discussion in Chapter 5 with the quantification of errors involved in paleomagnetic sampling and further applications of non-parametric regression based on neural differential equations.