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pro vyhledávání: '"Kevrekidis, George"'
Conformal Autoencoders are a neural network architecture that imposes orthogonality conditions between the gradients of latent variables towards achieving disentangled representations of data. In this letter we show that orthogonality relations withi
Externí odkaz:
http://arxiv.org/abs/2408.16138
For multiple scientific endeavors it is common to measure a phenomenon of interest in more than one ways. We make observations of objects from several different perspectives in space, at different points in time; we may also measure different propert
Externí odkaz:
http://arxiv.org/abs/2408.15344
Autor:
Kevrekidis, George A., Serino, Daniel A., Kaltenborn, Alexander, Gammel, J. Tinka, Burby, Joshua W., Klasky, Marc L.
Equations of State model relations between thermodynamic variables and are ubiquitous in scientific modelling, appearing in modern day applications ranging from Astrophysics to Climate Science. The three desired properties of a general Equation of St
Externí odkaz:
http://arxiv.org/abs/2406.19957
Autor:
Evangelou, Nikolaos, Giovanis, Dimitrios G., Kevrekidis, George A., Pavliotis, Grigorios A., Kevrekidis, Ioannis G.
Deriving closed-form, analytical expressions for reduced-order models, and judiciously choosing the closures leading to them, has long been the strategy of choice for studying phase- and noise-induced transitions for agent-based models (ABMs). In thi
Externí odkaz:
http://arxiv.org/abs/2310.19039
Any representation of data involves arbitrary investigator choices. Because those choices are external to the data-generating process, each choice leads to an exact symmetry, corresponding to the group of transformations that takes one possible repre
Externí odkaz:
http://arxiv.org/abs/2301.13724
Single-cell RNA-seq data allow the quantification of cell type differences across a growing set of biological contexts. However, pinpointing a small subset of genomic features explaining this variability can be ill-defined and computationally intract
Externí odkaz:
http://arxiv.org/abs/2207.14106
Autor:
Evangelou, Nikolaos, Wichrowski, Noah J., Kevrekidis, George A., Dietrich, Felix, Kooshkbaghi, Mahdi, McFann, Sarah, Kevrekidis, Ioannis G.
We present a data-driven approach to characterizing nonidentifiability of a model's parameters and illustrate it through dynamic as well as steady kinetic models. By employing Diffusion Maps and their extensions, we discover the minimal combinations
Externí odkaz:
http://arxiv.org/abs/2110.06717
Autor:
Dietrich, Felix, Makeev, Alexei, Kevrekidis, George, Evangelou, Nikolaos, Bertalan, Tom, Reich, Sebastian, Kevrekidis, Ioannis G.
We identify effective stochastic differential equations (SDE) for coarse observables of fine-grained particle- or agent-based simulations; these SDE then provide useful coarse surrogate models of the fine scale dynamics. We approximate the drift and
Externí odkaz:
http://arxiv.org/abs/2106.09004
Autor:
Kaloudis, Konstantinos, Kevrekidis, George A., Maltezou, Helena C., Anastassopoulou, Cleo, Tsakris, Athanasios, Russo, Lucia
Herein, we provide estimations for the effective reproduction number $R_e$ for the greater metropolitan area of Athens, Greece during the first wave of the pandemic (February 26-May 15, 2020). For our calculations, we implemented, in a comparative ap
Externí odkaz:
http://arxiv.org/abs/2012.14192
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