Zobrazeno 1 - 6
of 6
pro vyhledávání: '"Pratyush Bhatt"'
Publikováno v:
Advanced Modeling and Simulation in Engineering Sciences, Vol 10, Iss 1, Pp 1-35 (2023)
Abstract Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scal
Externí odkaz:
https://doaj.org/article/a86315a609b84c8e8b68d16335a483ec
Most science and engineering problems are modeled by time-dependent and parametrized nonlinear partial differential equations. Their resolution with traditional computational methods may be too expensive, especially in the context of predictions with
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b5b6d5d49e305ff5e8c4d0b6ab370769
https://doi.org/10.5194/egusphere-egu23-39
https://doi.org/10.5194/egusphere-egu23-39
Publikováno v:
Industry 4.0 and Climate Change ISBN: 9781003293576
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7e35cf9607e3da69ccc34107cb228618
https://doi.org/10.1201/9781003293576-8
https://doi.org/10.1201/9781003293576-8
Autor:
Sumeet Varghese, Pratyush Bhatt
Publikováno v:
Journal of Operations and Strategic Planning. 3:194-225
Formulating business strategies in times of economic uncertainty can be a challenging exercise because of rapid changes in market assumptions, projections, and conditions. Studies have shown that the repertory of strategic choices that businesses exp
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::42f43aad2278b35122bf66964f420abf
https://doi.org/10.1177/2516600x20967813
https://doi.org/10.1177/2516600x20967813
Publikováno v:
SSRN Electronic Journal.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::79960ede9339b340af6851411d503250
https://doi.org/10.2139/ssrn.4229792
https://doi.org/10.2139/ssrn.4229792
Autor:
Anoop M. Namboodiri, Pratyush Bhatt
Publikováno v:
ICAPR
In this paper, we propose a novel method for fast nearest neighbors retrieval in non-Euclidean and non-metric spaces. We organize the data into a hierarchical fashion that preserves the local similarity structure. A method to find the approximate nea
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8cfc65ed1ff4116b69348ee6dda7fb9f
https://doi.org/10.1109/icapr.2009.99
https://doi.org/10.1109/icapr.2009.99