no code implementations • 15 Dec 2023 • Alex Devonport, Peter Seiler, Murat Arcak
We then establish how an $H_\infty$ Gaussian process can serve as a prior for Bayesian system identification and as a probabilistic uncertainty model for probabilistic robust control.
no code implementations • 29 Nov 2022 • Alex Devonport, Peter Seiler, Murat Arcak
Complex-valued Gaussian processes are used in Bayesian frequency-domain system identification as prior models for regression.
no code implementations • 27 Nov 2022 • Hari Prasanna Das, Yu-Wen Lin, Utkarsha Agwan, Lucas Spangher, Alex Devonport, Yu Yang, Jan Drgona, Adrian Chong, Stefano Schiavon, Costas J. Spanos
In this work, we review the ways in which machine learning has been leveraged to make buildings smart and energy-efficient.
no code implementations • 18 Dec 2021 • Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
In addition to applying classical Vapnik-Chervonenkis (VC) dimension bound arguments, we apply the PAC-Bayes theorem by leveraging a formal connection between kernelized empirical inverse Christoffel functions and Gaussian process regression models.
no code implementations • 2 Nov 2021 • Jared Mejia, Alex Devonport, Murat Arcak
Reachability analysis is used to determine all possible states that a system acting under uncertainty may reach.
no code implementations • 28 Apr 2021 • Alex Devonport, Adnane Saoud, Murat Arcak
Symbolic control techniques aim to satisfy complex logic specifications.
no code implementations • 28 Apr 2021 • Alex Devonport, Forest Yang, Laurent El Ghaoui, Murat Arcak
We present an algorithm for data-driven reachability analysis that estimates finite-horizon forward reachable sets for general nonlinear systems using level sets of a certain class of polynomials known as Christoffel functions.
no code implementations • L4DC 2020 • Alex Devonport, Murat Arcak
Many practical systems are not amenable to the reachability methods that give guarantees of correctness, since they have dynamics that are strongly nonlinear, uncertain, and possibly unknown.