Machine learning and digital twins: monitoring and control for dynamic security in power systems

The reader of the chapter will be able to connect techniques from machine learning (ML) and digital twins (DTs) to gain insights for monitoring and control of (dynamic) security for electrical power systems. DTs are validated and verified high-fidelity (hf) models providing high simulation accuracy. DTs can be used for simulation of the...

Paving the way for the integration of synthesis, assessment, and design tools within an ontological framework

The constant development of new alternatives to treat waste aids in closing material loops towards the circular economy and improving sustainability through the use of new renewable materials and energy. This fact leads to the increasing need for decision-making tools for process synthesis and assessment, which can be addressed with an...

Learning About the Adversary

The evolving nature of the tactics, techniques, and procedures used by cyber adversaries have made signature and template based methods of modeling adversary behavior almost infeasible. We are moving into an era of data-driven autonomous cyber defense agents that learn contextually meaningful adversary behaviors from observables. In this chapter...