This book presents a wealth of machine learning techniques to make the control of robots more flexible and safe when interacting with humans.

The text is integrated with the industry standard neural network/adaptive system simulator NeuroSolutions. This allows the authors to demonstrate and reinforce key concepts using over 200 interactive examples.

Written by two acknowledged experts in this emerging field, this book offers researchers their first comprehensive introduction to adaptive sampling.

In this thesis, we focus on Bayesian learning for a particular class of models: high-dimensional nonlinear dynamical systems, which have been commonly used to predict a wide range of transient phenomena including fluid flows, heat transfer, ...

For example, each sample may require a costly PDE solve. In this work, we develop a series of novel methods for learning approximations to high-dimensional functions from limited data over arbitrary (known or unknown) domains.

An up-to-date and authoritative look at the ever-widening technical boundaries and influence of neural networks in dynamical systems, this volume is an indispensable resource for researchers in neural networks and a reference staple for ...

The text features benchmark problems throughout to offer insights and illustrate some of the computational implications.

... Adaptive Learning (FBOAL) for the collocation points in PINNs that adds and removes points based on PDEs residuals ... methods of adaptive sampling for the collocation points such as Residual-based Adaptive Distribution (RAD) and ...

"In networked control systems (NCS), wherein a communication network is used to close the feedback loop, the transmission of feedback signals and execution of the controller is currently carried out at periodic sampling instants.