Zobrazeno 1 - 10
of 329
pro vyhledávání: '"William L Ditto"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Diversity conveys advantages in nature, yet homogeneous neurons typically comprise the layers of artificial neural networks. Here we construct neural networks from neurons that learn their own activation functions, quickly diversify, and sub
Externí odkaz:
https://doaj.org/article/fe17220b9da9406db9e880adc97bcc8d
Publikováno v:
AIP Advances, Vol 13, Iss 8, Pp 085013-085013-7 (2023)
A particle confined to an impassable box is a paradigmatic and exactly solvable one-dimensional quantum system modeled by an infinite square well potential. Here, we explore some of its infinitely many generalizations to two dimensions, including par
Externí odkaz:
https://doaj.org/article/5389d461316443e792e5bb98fa7b2305
Publikováno v:
Chaos, Solitons & Fractals: X, Vol 5, Iss , Pp 100046- (2020)
We quantify how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. We train conventional and Hamiltonian neural networks on increasi
Externí odkaz:
https://doaj.org/article/1469f6cd2f2f42bba647acfa94d6fd14
Publikováno v:
PLoS ONE, Vol 15, Iss 3, p e0228534 (2020)
The core element of machine learning is a flexible, universal function approximator that can be trained and fit into the data. One of the main challenges in modern machine learning is to understand the role of nonlinearity and complexity in these uni
Externí odkaz:
https://doaj.org/article/c84f7b39a8334ab78a81e360716b495b
Publikováno v:
PLoS ONE, Vol 13, Iss 12, p e0209037 (2018)
Certain nonlinear systems can switch between dynamical attractors occupying different regions of phase space, under variation of parameters or initial states. In this work we exploit this feature to obtain reliable logic operations. With logic output
Externí odkaz:
https://doaj.org/article/3a5e10dd108444fc95a8ed431da022bf
Publikováno v:
Royal Society Open Science, Vol 4, Iss 1 (2017)
We develop a framework to uncover and analyse dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive datasets to eliminate erroneous data segments, application
Externí odkaz:
https://doaj.org/article/84647fd1e9ae42fd91aaa2d5de8ec8bf
Publikováno v:
The European Physical Journal Special Topics. 230:3403-3409
We first review ideas of harnessing chaotic attractors to implement robust and flexible logic gates, and recast these concepts in the context of tipping points. The central idea is as follows: The presence of tipping points in complex systems endows
Autor:
Anshul Choudhary, Scott T. Miller, Elliott G. Holliday, John F. Lindner, Sudeshna Sinha, William L. Ditto
Publikováno v:
Nonlinear Dynamics. 103:1553-1562
Conventional neural networks are universal function approximators, but they may need impractically many training data to approximate nonlinear dynamics. Recently introduced Hamiltonian neural networks can efficiently learn and forecast dynamical syst
Publikováno v:
Nonlinear Theory and Its Applications, IEICE. 12:134-142
The 5th Experimental Chaos Conference was a gathering of scientists and engineers who work on real-world systems that behave in a nonlinear and, often, chaotic fashion. The proceedings present discoveries of chaotic behavior, explanation of nonlinear