Zobrazeno 1 - 10
of 147
pro vyhledávání: '"Misra, Sidhant"'
Markov chain samplers designed to sample from multi-variable distributions often undesirably get stuck in specific regions of their state space. This causes such samplers to approximately sample from a metastable distribution which is usually quite d
Externí odkaz:
http://arxiv.org/abs/2410.13800
Constrained optimization problems arise in various engineering system operations such as inventory management and electric power grids. However, the requirement to repeatedly solve such optimization problems with uncertain parameters poses a signific
Externí odkaz:
http://arxiv.org/abs/2410.03085
Autor:
Bärtschi, Andreas, Caravelli, Francesco, Coffrin, Carleton, Colina, Jonhas, Eidenbenz, Stephan, Jayakumar, Abhijith, Lawrence, Scott, Lee, Minseong, Lokhov, Andrey Y., Mishra, Avanish, Misra, Sidhant, Morrell, Zachary, Mughal, Zain, Neill, Duff, Piryatinski, Andrei, Scheie, Allen, Vuffray, Marc, Zhang, Yu
The emergence of quantum computing technology over the last decade indicates the potential for a transformational impact in the study of quantum mechanical systems. It is natural to presume that such computing technologies would be valuable to large
Externí odkaz:
http://arxiv.org/abs/2406.06625
Analog Quantum Computers are promising tools for improving performance on applications such as modeling behavior of quantum materials, providing fast heuristic solutions to optimization problems, and simulating quantum systems. Due to the challenges
Externí odkaz:
http://arxiv.org/abs/2404.14501
The power flow equations are central to many problems in power system planning, analysis, and control. However, their inherent non-linearity and non-convexity present substantial challenges during problem-solving processes, especially for optimizatio
Externí odkaz:
http://arxiv.org/abs/2404.09876
The power flow equations are fundamental to power system planning, analysis, and control. However, the inherent non-linearity and non-convexity of these equations present formidable obstacles in problem-solving processes. To mitigate these challenges
Externí odkaz:
http://arxiv.org/abs/2404.04391
Solving linear systems is at the foundation of many algorithms. Recently, quantum linear system algorithms (QLSAs) have attracted great attention since they converge to a solution exponentially faster than classical algorithms in terms of the problem
Externí odkaz:
http://arxiv.org/abs/2403.19829
Natural gas consumption by users of pipeline networks is subject to increasing uncertainty that originates from the intermittent nature of electric power loads serviced by gas-fired generators. To enable computationally efficient optimization of gas
Externí odkaz:
http://arxiv.org/abs/2403.18124
Quantum computers hold promise for solving problems intractable for classical computers, especially those with high time and/or space complexity. The reduction of the power flow (PF) problem into a linear system of equations, allows formulation of qu
Externí odkaz:
http://arxiv.org/abs/2402.08617
This work presents an efficient data-driven method to construct probabilistic voltage envelopes (PVE) using power flow learning in grids with network contingencies. First, a network-aware Gaussian process (GP) termed Vertex-Degree Kernel (VDK-GP), de
Externí odkaz:
http://arxiv.org/abs/2310.00763