Zobrazeno 1 - 10
of 70
pro vyhledávání: '"Kooshkbaghi, Mahdi"'
Autor:
Kiyani, Elham, Kooshkbaghi, Mahdi, Shukla, Khemraj, Koneru, Rahul Babu, Li, Zhen, Bravo, Luis, Ghoshal, Anindya, Karniadakis, George Em, Karttunen, Mikko
The molten sand, a mixture of calcia, magnesia, alumina, and silicate, known as CMAS, is characterized by its high viscosity, density, and surface tension. The unique properties of CMAS make it a challenging material to deal with in high-temperature
Externí odkaz:
http://arxiv.org/abs/2307.09142
One of the main questions regarding complex systems at large scales concerns the effective interactions and driving forces that emerge from the detailed microscopic properties. Coarse-grained models aim to describe complex systems in terms of coarse-
Externí odkaz:
http://arxiv.org/abs/2203.16692
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
In this paper we present a systematic, data-driven approach to discovering "bespoke" coarse variables based on manifold learning algorithms. We illustrate this methodology with the classic Kuramoto phase oscillator model, and demonstrate how our mani
Externí odkaz:
http://arxiv.org/abs/2004.06053
Autor:
Lee, Seungjoon, Kooshkbaghi, Mahdi, Spiliotis, Konstantinos, Siettos, Constantinos I., Kevrekidis, Ioannis G.
Complex spatiotemporal dynamics of physicochemical processes are often modeled at a microscopic level (through e.g. atomistic, agent-based or lattice models) based on first principles. Some of these processes can also be successfully modeled at the m
Externí odkaz:
http://arxiv.org/abs/1909.05707
Publikováno v:
Chaos 30, 043108 (2020)
Data mining is routinely used to organize ensembles of short temporal observations so as to reconstruct useful, low-dimensional realizations of an underlying dynamical system. In this paper, we use manifold learning to organize unstructured ensembles
Externí odkaz:
http://arxiv.org/abs/1810.12952
Autor:
Holiday, Alexander, Kooshkbaghi, Mahdi, Bello-Rivas, Juan M., Gear, C. William, Zagaris, Antonios, Kevrekidis, Ioannis G.
Large scale dynamical systems (e.g. many nonlinear coupled differential equations) can often be summarized in terms of only a few state variables (a few equations), a trait that reduces complexity and facilitates exploration of behavioral aspects of
Externí odkaz:
http://arxiv.org/abs/1807.08338
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Akademický článek
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Autor:
Moosmüller, Caroline, Tralie, Christopher J., Kooshkbaghi, Mahdi, Belkhatir, Zehor, Pouryahya, Maryam, Reyes, José, Deasy, Joseph O., Tannenbaum, Allen R., Kevrekidis, Ioannis G.
Publikováno v:
In IFAC PapersOnLine 2021 54(9):488-495