Zobrazeno 1 - 10
of 255
pro vyhledávání: '"Ditto, William L."'
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 part
Externí odkaz:
http://arxiv.org/abs/2302.01413
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 subsequently
Externí odkaz:
http://arxiv.org/abs/2204.04348
Autor:
Choudhary, Anshul, Lindner, John F., Holliday, Elliott G., Miller, Scott T., Sinha, Sudeshna, Ditto, William L.
Conventional neural networks are universal function approximators, but because they are unaware of underlying symmetries or physical laws, they may need impractically many training data to approximate nonlinear dynamics. Recently introduced Hamiltoni
Externí odkaz:
http://arxiv.org/abs/2010.15201
We detail how incorporating physics into neural network design can significantly improve the learning and forecasting of dynamical systems, even nonlinear systems of many dimensions. A map building perspective elucidates the superiority of Hamiltonia
Externí odkaz:
http://arxiv.org/abs/2008.04214
Autor:
Choudhary, Anshul, Lindner, John F., Holliday, Elliott G., Miller, Scott T., Sinha, Sudeshna, Ditto, William L.
Publikováno v:
Phys. Rev. E 101, 062207 (2020)
Conventional artificial neural networks are powerful tools in science and industry, but they can fail when applied to nonlinear systems where order and chaos coexist. We use neural networks that incorporate the structures and symmetries of Hamiltonia
Externí odkaz:
http://arxiv.org/abs/1912.01958
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:
http://arxiv.org/abs/1811.10029
We develop a framework to uncover and analyze dynamical anomalies from massive, nonlinear and non-stationary time series data. The framework consists of three steps: preprocessing of massive data sets to eliminate erroneous data segments, application
Externí odkaz:
http://arxiv.org/abs/1612.07840
Autor:
Lindner, John F., Kohar, Vivek, Kia, Behnam, Hippke, Michael, Learned, John G., Ditto, William L.
Dramatically improved data from observatories like the CoRoT and Kepler spacecraft have recently facilitated nonlinear time series analysis and phenomenological modeling of variable stars, including the search for strange (aka fractal) or chaotic dyn
Externí odkaz:
http://arxiv.org/abs/1510.04719
Autor:
Lindner, John F., Kohar, Vivek, Kia, Behnam, Hippke, Michael, Learned, John G., Ditto, William L.
Publikováno v:
Phys. Rev. Lett. 114, 054101 (2015)
The unprecedented light curves of the Kepler space telescope document how the brightness of some stars pulsates at primary and secondary frequencies whose ratios are near the golden mean, the most irrational number. A nonlinear dynamical system drive
Externí odkaz:
http://arxiv.org/abs/1501.01747
Publikováno v:
In Chaos, Solitons & Fractals: X March 2020 5
