Zobrazeno 1 - 10
of 1 485
pro vyhledávání: '"Rivas, A. M."'
We introduce an efficient neural network (NN) architecture for classifying wave functions in terms of their localization. Our approach integrates a versatile quantum phase space parametrization leading to a custom 'quantum' NN, with the pattern recog
Externí odkaz:
http://arxiv.org/abs/2406.00363
Autor:
da Silva, M. P., Ryan-Anderson, C., Bello-Rivas, J. M., Chernoguzov, A., Dreiling, J. M., Foltz, C., Frachon, F., Gaebler, J. P., Gatterman, T. M., Grans-Samuelsson, L., Hayes, D., Hewitt, N., Johansen, J., Lucchetti, D., Mills, M., Moses, S. A., Neyenhuis, B., Paz, A., Pino, J., Siegfried, P., Strabley, J., Sundaram, A., Tom, D., Wernli, S. J., Zanner, M., Stutz, R. P., Svore, K. M.
The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely pro
Externí odkaz:
http://arxiv.org/abs/2404.02280
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
Steady states are invaluable in the study of dynamical systems. High-dimensional dynamical systems, due to a separation of time-scales, often evolve towards a lower dimensional manifold $M$. We introduce an approach to locate saddle points (and other
Externí odkaz:
http://arxiv.org/abs/2309.16920
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
The out-of-time order correlator (OTOC) has been widely studied in closed quantum systems. However, there are very few studies for open systems and they are mainly focused on isolating the effects of scrambling from those of decoherence. Adopting a d
Externí odkaz:
http://arxiv.org/abs/2211.06353
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