Zobrazeno 1 - 10
of 52
pro vyhledávání: '"86A08"'
Autor:
Danis, Mustafa Engin, Truong, Duc P., DeSantis, Derek, Petersen, Mark, Rasmussen, Kim O., Alexandrov, Boian S.
In this paper, we introduce a high-order tensor-train (TT) finite volume method for the Shallow Water Equations (SWEs). We present the implementation of the $3^{rd}$ order Upwind and the $5^{th}$ order Upwind and WENO reconstruction schemes in the TT
Externí odkaz:
http://arxiv.org/abs/2408.03483
Autor:
Priyanka, M., Muthukumar, P.
The reduction in coral reef densities, characterized by the proliferation of macroalgae, has emerged as a global threat. In this paper, we present a discrete-time coral reef dynamical model that incorporates macroalgae. We explore all ecologically po
Externí odkaz:
http://arxiv.org/abs/2405.07491
We investigate ocean circulation changes through the lens of data assimilation using a reduced-order model. Our primary interest lies in the Stommel box model which reveals itself to be one of the most practicable models that has the ability of repro
Externí odkaz:
http://arxiv.org/abs/2404.07134
Autor:
Flandoli, Franco, Huang, Ruojun
A new model for particle coalescence in a turbulent environment is presented, aiming to prove that turbulence enhances coalescence, for small-moderate Stokes numbers. The model is non-inertial, namely the velocity of particles is the velocity of the
Externí odkaz:
http://arxiv.org/abs/2404.05233
Autor:
Andreou, Marios, Chen, Nan
Studying the response of a climate system to perturbations has practical significance. Standard methods in computing the trajectory-wise deviation caused by perturbations may suffer from the chaotic nature that makes the model error dominate the true
Externí odkaz:
http://arxiv.org/abs/2401.03281
The risk of extreme coastal flooding to Bangladesh's low-lying and densely populated coastal regions, already vulnerable to tropical cyclones, remains poorly quantified under a warming climate. Here, using a statistical-physical downscaling approach,
Externí odkaz:
http://arxiv.org/abs/2312.06051
Autor:
Moews, Ben, Gieschen, Antonia
Spatio-temporal clustering occupies an established role in various fields dealing with geospatial analysis, spanning from healthcare analysis to environmental science. One major challenge are applications in which cluster assignments are dependent on
Externí odkaz:
http://arxiv.org/abs/2311.04290
Complex Earth System Models are widely utilised to make conditional statements about the future climate under some assumptions about changes in future atmospheric greenhouse gas concentrations; these statements are often referred to as climate projec
Externí odkaz:
http://arxiv.org/abs/2310.05967
Autor:
Mukkavilli, S. Karthik, Civitarese, Daniel Salles, Schmude, Johannes, Jakubik, Johannes, Jones, Anne, Nguyen, Nam, Phillips, Christopher, Roy, Sujit, Singh, Shraddha, Watson, Campbell, Ganti, Raghu, Hamann, Hendrik, Nair, Udaysankar, Ramachandran, Rahul, Weldemariam, Kommy
Machine learning and deep learning methods have been widely explored in understanding the chaotic behavior of the atmosphere and furthering weather forecasting. There has been increasing interest from technology companies, government institutions, an
Externí odkaz:
http://arxiv.org/abs/2309.10808
We explore the implementation of deep learning techniques for precise building damage assessment in the context of natural hazards, utilizing remote sensing data. The xBD dataset, comprising diverse disaster events from across the globe, serves as th
Externí odkaz:
http://arxiv.org/abs/2309.01066