In these Lecture Notes, we provide a comprehensive introduction to the most recent
advances in the application of machine learning methods in quantum sciences. We cover …

In molecular dynamics (MD), neural network (NN) potentials trained bottom-up on quantum
mechanical data have seen tremendous success recently. Top-down approaches that learn …

Inverse analysis has been utilized to understand unknown underground geological
properties by matching the observational data with simulators. To overcome the …

Kohn–Sham regularizer (KSR) is a differentiable machine learning approach to finding the
exchange-correlation functional in Kohn–Sham density functional theory that works for …

Scientific research communities are embracing AI-based solutions to target tractable
scientific tasks and improve research work flows. However, the development and evaluation …

This study presents a policy optimisation framework for structured nonlinear control of
continuous-time (deterministic) dynamic systems. The proposed approach prescribes a …

The value of neuromorphic computers depends crucially on our ability to program them for
relevant tasks. Currently, neuromorphic computers are mostly limited to machine learning …

Numerical analysis has advanced greatly since it began as a way of creating methods to
approximate answers to mathematical questions. This book aims to bring students closer to …

Optimization is showcased as an accessible and powerful tool in this attractive and
instructive text. Optimization techniques are presented with mathematical motivation, without …

We present a linear algebra formulation of backpropagation which allows the calculation of
gradients by using a generically written “backslash” or Gaussian elimination on triangular …