The accuracy and reliability of physics-based lithium-ion battery degradation models are
limited by incomplete understanding of degradation mechanisms. This article presents …

Differentiable Programming for scientific machine learning (SciML) has recently seen
considerable interest and success, as it directly embeds neural networks inside PDEs, often …

Radau IIA methods, specifically the adaptive order radau method in Fortran due to Hairer,
are known to be state-of-the-art for the high-accuracy solution of highly stiff ordinary …

For reasons of computational constraint, most global ocean circulation models used for
Earth System Modeling still rely on parameterizations of sub-grid processes, and limitations …

This research employs Universal Differential Equations (UDEs) alongside differentiable
physics to model viscoelastic fluids, merging conventional differential equations, neural …

Kolmogorov-Arnold Networks (KANs) have recently emerged as a potential alternative to
multi-layer perceptrons (MLPs), leveraging the Kolmogorov Representation Theorem to …

It is imperative to decarbonize the power system as quickly as possible to respond to the
threat of climate change. In pursuit of this goal, power systems around the world are …

This compilation thesis explores the intersection of machine learning and quantum
computing, focusing on optimizing quantum systems and exploring use-cases for quantum …

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 …

The following aims to be a pragmatic introduction to hardware-friendly learning of implicit
models, which encompass a broad class of models from feedforward nets to physical …