Zobrazeno 1 - 10
of 59
pro vyhledávání: '"Bello-Rivas, Juan M."'
A valuable step in the modeling of multiscale dynamical systems in fields such as computational chemistry, biology, materials science and more, is the representative sampling of the phase space over long timescales of interest; this task is not, howe
Externí odkaz:
http://arxiv.org/abs/2312.05715
Autor:
Evangelou, Nikolaos, Cui, Tianqi, Bello-Rivas, Juan M., Makeev, Alexei, Kevrekidis, Ioannis G.
We study the tipping point collective dynamics of an adaptive susceptible-infected-susceptible (SIS) epidemiological network in a data-driven, machine learning-assisted manner. We identify a parameter-dependent effective stochastic differential equat
Externí odkaz:
http://arxiv.org/abs/2311.00797
Autor:
Fabiani, Gianluca, Evangelou, Nikolaos, Cui, Tianqi, Bello-Rivas, Juan M., Martin-Linares, Cristina P., Siettos, Constantinos, Kevrekidis, Ioannis G.
We present a machine learning (ML)-assisted framework bridging manifold learning, neural networks, Gaussian processes, and Equation-Free multiscale modeling, for (a) detecting tipping points in the emergent behavior of complex systems, and (b) charac
Externí odkaz:
http://arxiv.org/abs/2309.14334
Autor:
Redman, William T., Bello-Rivas, Juan M., Fonoberova, Maria, Mohr, Ryan, Kevrekidis, Ioannis G., Mezić, Igor
Study of the nonlinear evolution deep neural network (DNN) parameters undergo during training has uncovered regimes of distinct dynamical behavior. While a detailed understanding of these phenomena has the potential to advance improvements in trainin
Externí odkaz:
http://arxiv.org/abs/2302.09160
Finding saddle points of dynamical systems is an important problem in practical applications such as the study of rare events of molecular systems. Gentlest ascent dynamics (GAD) is one of a number of algorithms in existence that attempt to find sadd
Externí odkaz:
http://arxiv.org/abs/2302.04426
Autor:
Psarellis, Yorgos M., Lee, Seungjoon, Bhattacharjee, Tapomoy, Datta, Sujit S., Bello-Rivas, Juan M., Kevrekidis, Ioannis G.
E. coli chemotactic motion in the presence of a chemoattractant field has been extensively studied using wet laboratory experiments, stochastic computational models as well as partial differential equation-based models (PDEs). The most challenging st
Externí odkaz:
http://arxiv.org/abs/2208.11853
Sampling the phase space of molecular systems -- and, more generally, of complex systems effectively modeled by stochastic differential equations -- is a crucial modeling step in many fields, from protein folding to materials discovery. These problem
Externí odkaz:
http://arxiv.org/abs/2208.10715
Autor:
Evangelou, Nikolaos, Dietrich, Felix, Bello-Rivas, Juan M., Yeh, Alex, Stein, Rachel, Bevan, Michael A., Kevrekidis, Ioannis G.
We construct a reduced, data-driven, parameter dependent effective Stochastic Differential Equation (eSDE) for electric-field mediated colloidal crystallization using data obtained from Brownian Dynamics Simulations. We use Diffusion Maps (a manifold
Externí odkaz:
http://arxiv.org/abs/2205.00286
We introduce a method to successively locate equilibria (steady states) of dynamical systems on Riemannian manifolds. The manifolds need not be characterized by an a priori known atlas or by the zeros of a smooth map. Instead, they can be defined by
Externí odkaz:
http://arxiv.org/abs/2204.10413
We discuss the correspondence between Gaussian process regression and Geometric Harmonics, two similar kernel-based methods that are typically used in different contexts. Research communities surrounding the two concepts often pursue different goals.
Externí odkaz:
http://arxiv.org/abs/2110.02296