Nonlinear Data-Enabled Prediction and Control

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For robots to operate in unstructured environments, they are required to interact with objects through contact. Those contacts may be used to push objects to the side, deform objects, or manipulate objects in-hand. This thesis addresses the problem of cont ...

The transition to sustainable energy demands a fundamental redesign of the electrical grid, especially at the distribution level. With the increasing deployment of distributed energy resources, the development of reliable models, accurate state estimation ...

Explicit time integration schemes coupled with Galerkin discretizations of time-dependent partial differential equations require solving a linear system with the mass matrix at each time step. For applications in structural dynamics, the solution of the li ...

Empowered by ever-increasing computational power and algorithmic developments, electronic-structure simulations continue to drive research and innovation in materials science. In this context, ab-initio calculations offer an unbiased platform for the under ...

The thesis presents methods for controlling and planning distributed energy resources (DERs) in active distribution networks (ADNs). It deals with three main challenges: (i) developing and experimentally validating grid-aware real-time control frameworks, ...

Herein we report a multi-zone, heating, ventilation and air-conditioning (HVAC) control case study of an industrial plant responsible for cooling a hospital surgery center. The adopted approach to guaranteeing thermal comfort and reducing electrical energy ...

Global spectral methods offer the potential to compute solutions of partial differential equations numerically to very high accuracy. In this work, we develop a novel global spectral method for linear partial differential equations on cubes by extending th ...

Replacing poorly performing existing controllers with smarter solutions will decrease the energy intensity of the building sector. Recently, controllers based on Deep Reinforcement Learning (DRL) have been shown to be more effective than conventional basel ...

In communication systems, there are many tasks, like modulation classification, for which Deep Neural Networks (DNNs) have obtained promising performance. However, these models have been shown to be susceptible to adversarial perturbations, namely impercep ...

Multimode optical fibers are the backbone of telecommunication and medical imaging. When light with high intensity travels through a multimode fiber, photons and matter start to interact and propa-gation becomes nonlinear. The nonlinear propagation of ligh ...

This thesis presents a combined experimental and theoretical study of the classical and quantum magnetization dynamics in single magnetic adatoms and molecules, and on the classical and quantum coherent control thereof. First, a detailed description of t ...

Absorbing airborne noise at frequencies below 300 Hz is a particularly vexing problem due to the absence of natural sound absorbing materials at these frequencies. The prevailing solution for low-frequency sound absorption is the use of passive narrow-band ...

Enabling analysis of non-linear systems in linear form, the Koopman operator has been shown to be a powerful tool for system identification and controller design. However, current data-driven methods cannot provide quantification of model uncertainty given ...

Nonlinear Data-Enabled Prediction and Control

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A Stochastic Approach to Contact-rich Manipulation

Estimation and control under uncertainty with application to power systems

A mathematical theory for mass lumping and its generalization with applications to isogeometric analysis

Spectral and thermodynamic properties of interacting electrons with dynamical functionals

Methods for Grid-aware Operation and Planning of Active Distribution Networks

Experimental data-driven model predictive control of a hospital HVAC system during regular use

Fast global spectral methods for three-dimensional partial differential equations

Near-optimal Deep Reinforcement Learning Policies from Data for Zone Temperature Control

SafeAMC: Adversarial training for robust modulation classification models

Exploring nonlinearities in multimode optical fibers for lasers and computing

Quantum dynamics in individual surface spins

Improving sound absorption through nonlinear active electroacoustic resonators

On Gaussian Process Based Koopman Operators