Zobrazeno 1 - 10
of 162
pro vyhledávání: '"Paulson, Joel"'
Autor:
Sorourifar, Farshud, Rouabah, Mohamed Taha, Belaloui, Nacer Eddine, Louamri, Mohamed Messaoud, Chamaki, Diana, Gustafson, Erik J., Tubman, Norm M., Paulson, Joel A., Neira, David E. Bernal
Variational Quantum Eigensolvers (VQEs) represent a promising approach to computing molecular ground states and energies on modern quantum computers. These approaches use a classical computer to optimize the parameters of a trial wave function, while
Externí odkaz:
http://arxiv.org/abs/2407.07963
Autor:
Sorourifar, Farshud, Chamaki, Diana, Tubman, Norm M., Paulson, Joel A., Neira, David E. Bernal
Quantum computers currently rely on a hybrid quantum-classical approach known as Variational Quantum Algorithms (VQAs) to solve problems. Still, there are several challenges with VQAs on the classical computing side: it corresponds to a black-box opt
Externí odkaz:
http://arxiv.org/abs/2406.14627
This article is devoted to providing a review of mathematical formulations in which Polynomial Chaos Theory (PCT) has been incorporated into stochastic model predictive control (SMPC). In the past decade, PCT has been shown to provide a computational
Externí odkaz:
http://arxiv.org/abs/2406.10734
Novelty search (NS) refers to a class of exploration algorithms that automatically uncover diverse system behaviors through simulations or experiments. Systematically obtaining diverse outcomes is a key component in many real-world design problems su
Externí odkaz:
http://arxiv.org/abs/2406.03616
This work addresses data-driven inverse optimization (IO), where the goal is to estimate unknown parameters in an optimization model from observed decisions that can be assumed to be optimal or near-optimal solutions to the optimization problem. The
Externí odkaz:
http://arxiv.org/abs/2405.17875
Autor:
Tang, Wei-Ting, Paulson, Joel A.
Bayesian optimization (BO) is a popular approach for optimizing expensive-to-evaluate black-box objective functions. An important challenge in BO is its application to high-dimensional search spaces due in large part to the curse of dimensionality. O
Externí odkaz:
http://arxiv.org/abs/2405.07760
Autor:
Paulson, Joel A., Tsay, Calvin
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. In this paper, we provide
Externí odkaz:
http://arxiv.org/abs/2401.16373
Molecular property optimization (MPO) problems are inherently challenging since they are formulated over discrete, unstructured spaces and the labeling process involves expensive simulations or experiments, which fundamentally limits the amount of av
Externí odkaz:
http://arxiv.org/abs/2401.01398
Autor:
Nghiem, Truong X., Drgoňa, Ján, Jones, Colin, Nagy, Zoltan, Schwan, Roland, Dey, Biswadip, Chakrabarty, Ankush, Di Cairano, Stefano, Paulson, Joel A., Carron, Andrea, Zeilinger, Melanie N., Cortez, Wenceslao Shaw, Vrabie, Draguna L.
Physics-informed machine learning (PIML) is a set of methods and tools that systematically integrate machine learning (ML) algorithms with physical constraints and abstract mathematical models developed in scientific and engineering domains. As oppos
Externí odkaz:
http://arxiv.org/abs/2306.13867
Autor:
Lu, Congwen, Paulson, Joel A.
This paper investigates the problem of efficient constrained global optimization of hybrid models that are a composition of a known white-box function and an expensive multi-output black-box function subject to noisy observations, which often arises
Externí odkaz:
http://arxiv.org/abs/2305.03824