no code implementations • 13 May 2024 • Wei-Ting Tang, Joel A. Paulson
One way to overcome this challenge is to focus on local BO methods that aim to efficiently learn gradients, which have shown strong empirical performance on a variety of high-dimensional problems including policy search in reinforcement learning (RL).
no code implementations • 29 Jan 2024 • Joel A. Paulson, Calvin Tsay
Bayesian optimization (BO) is a powerful technology for optimizing noisy expensive-to-evaluate black-box functions, with a broad range of real-world applications in science, engineering, economics, manufacturing, and beyond.
no code implementations • 2 Jan 2024 • Farshud Sorourifar, Thomas Banker, Joel A. Paulson
In this work, we show that such methods have a tendency to "get stuck," which we hypothesize occurs since the mapping from the encoded space to property values is not necessarily well-modeled by a Gaussian process.
no code implementations • 24 Jun 2023 • Truong X. Nghiem, Ján Drgoňa, Colin Jones, Zoltan Nagy, Roland Schwan, Biswadip Dey, Ankush Chakrabarty, Stefano Di Cairano, Joel A. Paulson, Andrea Carron, Melanie N. Zeilinger, Wenceslao Shaw Cortez, Draguna L. Vrabie
Specifically, the paper covers an overview of the theory, fundamental concepts and methods, tools, and applications on topics of: 1) physics-informed learning for system identification; 2) physics-informed learning for control; 3) analysis and verification of PIML models; and 4) physics-informed digital twins.
1 code implementation • 5 May 2023 • Congwen Lu, Joel A. Paulson
Since these bounds depend sublinearly on the number of iterations under some regularity assumptions, we establis bounds on the convergence rate to the optimal solution of the original constrained problem.
2 code implementations • 3 Sep 2021 • Jared O'Leary, Joel A. Paulson, Ali Mesbah
Stochastic differential equations (SDEs) are used to describe a wide variety of complex stochastic dynamical systems.