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pro vyhledávání: '"Penwarden A"'
Physics-informed machine learning (PIML) as a means of solving partial differential equations (PDE) has garnered much attention in the Computational Science and Engineering (CS&E) world. This topic encompasses a broad array of methods and models aime
Externí odkaz:
http://arxiv.org/abs/2402.11126
Autor:
Maple, Carsten, Szpruch, Lukasz, Epiphaniou, Gregory, Staykova, Kalina, Singh, Simran, Penwarden, William, Wen, Yisi, Wang, Zijian, Hariharan, Jagdish, Avramovic, Pavle
This report examines Artificial Intelligence (AI) in the financial sector, outlining its potential to revolutionise the industry and identify its challenges. It underscores the criticality of a well-rounded understanding of AI, its capabilities, and
Externí odkaz:
http://arxiv.org/abs/2308.16538
Neural operator learning as a means of mapping between complex function spaces has garnered significant attention in the field of computational science and engineering (CS&E). In this paper, we apply Neural operator learning to the time-of-flight ult
Externí odkaz:
http://arxiv.org/abs/2304.03297
Autor:
Penwarden, Michael, Jagtap, Ameya D., Zhe, Shandian, Karniadakis, George Em, Kirby, Robert M.
Publikováno v:
Journal of Computational Physics, 493, 2023, 112464
Physics-informed neural networks (PINNs) as a means of solving partial differential equations (PDE) have garnered much attention in the Computational Science and Engineering (CS&E) world. However, a recent topic of interest is exploring various train
Externí odkaz:
http://arxiv.org/abs/2302.14227
Autor:
Shukla, Khemraj, Oommen, Vivek, Peyvan, Ahmad, Penwarden, Michael, Bravo, Luis, Ghoshal, Anindya, Kirby, Robert M., Karniadakis, George Em
Deep neural operators, such as DeepONets, have changed the paradigm in high-dimensional nonlinear regression from function regression to (differential) operator regression, paving the way for significant changes in computational engineering applicati
Externí odkaz:
http://arxiv.org/abs/2302.00807
Autor:
Li, Shibo, Penwarden, Michael, Xu, Yiming, Tillinghast, Conor, Narayan, Akil, Kirby, Robert M., Zhe, Shandian
Physics-informed neural networks (PINNs) are emerging as popular mesh-free solvers for partial differential equations (PDEs). Recent extensions decompose the domain, apply different PINNs to solve the problem in each subdomain, and stitch the subdoma
Externí odkaz:
http://arxiv.org/abs/2210.12669
Publikováno v:
Journal of Computational Physics, Volume 477, 2023, 111912
Physics-informed neural networks (PINNs) as a means of discretizing partial differential equations (PDEs) are garnering much attention in the Computational Science and Engineering (CS&E) world. At least two challenges exist for PINNs at present: an u
Externí odkaz:
http://arxiv.org/abs/2110.13361
Publikováno v:
Journal of Computational Physics Volume 451, 15 February 2022, 110844
Multifidelity simulation methodologies are often used in an attempt to judiciously combine low-fidelity and high-fidelity simulation results in an accuracy-increasing, cost-saving way. Candidates for this approach are simulation methodologies for whi
Externí odkaz:
http://arxiv.org/abs/2106.13361
Autor:
Shukla, Khemraj, Oommen, Vivek, Peyvan, Ahmad, Penwarden, Michael, Plewacki, Nicholas, Bravo, Luis, Ghoshal, Anindya, Kirby, Robert M., Karniadakis, George Em
Publikováno v:
In Engineering Applications of Artificial Intelligence March 2024 129
Publikováno v:
BMC Public Health, Vol 23, Iss 1, Pp 1-11 (2023)
Abstract Background Social vulnerability occurs when the disadvantage conveyed by poor social conditions determines the degree to which one’s life and livelihood are at risk from a particular and identifiable event in health, nature, or society. A
Externí odkaz:
https://doaj.org/article/58643711b15b4d11b1f35c8fb165a4d1