Zobrazeno 1 - 10
of 681
pro vyhledávání: '"Brunton, Steven L"'
Autor:
Zhelyeznuyakov, Maksym, Fröch, Johannes E., Colburn, Shane, Brunton, Steven L., Majumdar, Arka
Computer vision tasks require processing large amounts of data to perform image classification, segmentation, and feature extraction. Optical preprocessors can potentially reduce the number of floating point operations required by computer vision tas
Externí odkaz:
http://arxiv.org/abs/2411.08995
Vortex shedding is an important physical phenomenon observed across many spatial and temporal scales in fluids. Previous experimental and theoretical studies have established a hierarchy of local and global reduced-order models for vortex shedding ba
Externí odkaz:
http://arxiv.org/abs/2411.08277
We consider the relationship between modal representations obtained from data-driven decomposition methods and Lagrangian Coherent Structures (LCSs). Mode sensitivity is used to describe this analysis as an extension of the model sensitivity framewor
Externí odkaz:
http://arxiv.org/abs/2410.20802
The accurate quantification of wall-shear stress dynamics is of substantial importance for various applications in fundamental and applied research, spanning areas from human health to aircraft design and optimization. Despite significant progress in
Externí odkaz:
http://arxiv.org/abs/2409.03933
Interpreting motion captured in image sequences is crucial for a wide range of computer vision applications. Typical estimation approaches include optical flow (OF), which approximates the apparent motion instantaneously in a scene, and multiple obje
Externí odkaz:
http://arxiv.org/abs/2408.16190
Autor:
Conti, Paolo, Kneifl, Jonas, Manzoni, Andrea, Frangi, Attilio, Fehr, Jörg, Brunton, Steven L., Kutz, J. Nathan
The simulation of many complex phenomena in engineering and science requires solving expensive, high-dimensional systems of partial differential equations (PDEs). To circumvent this, reduced-order models (ROMs) have been developed to speed up computa
Externí odkaz:
http://arxiv.org/abs/2405.20905
Autor:
Vinuesa, Ricardo, Rabault, Jean, Azizpour, Hossein, Bauer, Stefan, Brunton, Bingni W., Elofsson, Arne, Jarlebring, Elias, Kjellstrom, Hedvig, Markidis, Stefano, Marlevi, David, Cinnella, Paola, Brunton, Steven L.
Technological advancements have substantially increased computational power and data availability, enabling the application of powerful machine-learning (ML) techniques across various fields. However, our ability to leverage ML methods for scientific
Externí odkaz:
http://arxiv.org/abs/2405.04161
Publikováno v:
Journal of Computational Physics, Volume 516, 2024, 113371.
The ability to measure differences in collected data is of fundamental importance for quantitative science and machine learning, motivating the establishment of metrics grounded in physical principles. In this study, we focus on the development of su
Externí odkaz:
http://arxiv.org/abs/2404.14347
Deep reinforcement learning (DRL) has shown significant promise for uncovering sophisticated control policies that interact in environments with complicated dynamics, such as stabilizing the magnetohydrodynamics of a tokamak fusion reactor or minimiz
Externí odkaz:
http://arxiv.org/abs/2403.09110
The Bellman equation and its continuous form, the Hamilton-Jacobi-Bellman (HJB) equation, are ubiquitous in reinforcement learning (RL) and control theory. However, these equations quickly become intractable for systems with high-dimensional states a
Externí odkaz:
http://arxiv.org/abs/2403.02290