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pro vyhledávání: '"Surasinghe, Sudam"'
Autor:
Surasinghe, Sudam, Manivannan, Swathi Nachiar, Scarpino, Samuel V., Crawford, Lorin, Ogbunugafor, C. Brandon
Mathematical modelling has served a central role in studying how infectious disease transmission manifests at the population level. These models have demonstrated the importance of population-level factors like social network heterogeneity on structu
Externí odkaz:
http://arxiv.org/abs/2409.09096
Models are often employed to integrate knowledge about epidemics across scales and simulate disease dynamics. While these approaches have played a central role in studying the mechanics underlying epidemics, we lack ways to reliably predict how the r
Externí odkaz:
http://arxiv.org/abs/2311.04369
Autor:
Dias, Sathsara, Surasinghe, Sudam, Priyankara, Kanaththa, Budišić, Marko, Pratt, Larry, Sanchez-Garrido, José C., Bollt, Erik M.
The Strait of Gibraltar is a region characterized by intricate oceanic sub-mesoscale features, influenced by topography, tidal forces, instabilities, and nonlinear hydraulic processes, all governed by the nonlinear equations of fluid motion. In this
Externí odkaz:
http://arxiv.org/abs/2311.01377
Inference of transfer operators from data is often formulated as a classical problem that hinges on the Ulam method. The conventional description, known as the Ulam-Galerkin method, involves projecting onto basis functions represented as characterist
Externí odkaz:
http://arxiv.org/abs/2210.03124
Autor:
Surasinghe, Sudam, Bollt, Erik M.
A data-driven analysis method known as dynamic mode decomposition (DMD) approximates the linear Koopman operator on projected space. In the spirit of Johnson-Lindenstrauss Lemma, we will use random projection to estimate the DMD modes in reduced dime
Externí odkaz:
http://arxiv.org/abs/2110.01718
Autor:
Surasinghe, Sudam, Bollt, Erik M.
Publikováno v:
Entropy 2020, 22, 396
Causal inference is perhaps one of the most fundamental concepts in science, beginning originally from the works of some of the ancient philosophers, through today, but also weaved strongly in current work from statisticians, machine learning experts
Externí odkaz:
http://arxiv.org/abs/2002.02078
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Akademický článek
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Autor:
Surasinghe, Sudam, M. Bollt, Erik
Publikováno v:
Mathematics (2227-7390); Nov2021, Vol. 9 Issue 21, p2803, 1p