Zobrazeno 1 - 10
of 56
pro vyhledávání: '"Peter Y., Lu"'
Publikováno v:
Nature Communications, Vol 14, Iss 1, Pp 1-11 (2023)
Abstract Conservation laws are key theoretical and practical tools for understanding, characterizing, and modeling nonlinear dynamical systems. However, for many complex systems, the corresponding conserved quantities are difficult to identify, makin
Externí odkaz:
https://doaj.org/article/615615c8c9fc4b6f9642d59da2c3949b
Publikováno v:
Communications Physics, Vol 5, Iss 1, Pp 1-7 (2022)
Nonlinear dynamical systems are ubiquitous in nature and play an essential role in science, from providing models for the weather forecast to describing the chaotic behavior of plasma in nuclear reactors. This paper introduces an artificial intellige
Externí odkaz:
https://doaj.org/article/3a05bbfb257b4b02b5fdf8c893e28974
Extracting Interpretable Physical Parameters from Spatiotemporal Systems Using Unsupervised Learning
Publikováno v:
Physical Review X, Vol 10, Iss 3, p 031056 (2020)
Experimental data are often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by their intricate dynamics. Modern machine learning methods are particula
Externí odkaz:
https://doaj.org/article/8af1c2c38beb474290e0c72ae84757d1
Identifying the governing equations of a nonlinear dynamical system is key to both understanding the physical features of the system and constructing an accurate model of the dynamics that generalizes well beyond the available data. We propose a mach
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ea73ed72697e65549d3ed7f86666b11f
Autor:
Michael Gilbert, Vladimir Ceperic, Li Jing, Peter Y. Lu, Srijon Mukherjee, Samuel Kim, Marin Soljacic
Publikováno v:
IEEE transactions on neural networks and learning systems. 32(9)
Symbolic regression is a powerful technique that can discover analytical equations that describe data, which can lead to explainable models and generalizability outside of the training data set. In contrast, neural networks have achieved amazing leve
Autor:
Peter Y. Lu, Pierre F. J. Lermusiaux
Publikováno v:
Physica D: Nonlinear Phenomena. 427:133003
A new methodology for rigorous Bayesian learning of high-dimensional stochastic dynamical models is developed. The methodology performs parallelized computation of marginal likelihoods for multiple candidate models, integrating over all state variabl
Extracting Interpretable Physical Parameters from Spatiotemporal Systems using Unsupervised Learning
Publikováno v:
Physical Review X, Vol 10, Iss 3, p 031056 (2020)
Experimental data is often affected by uncontrolled variables that make analysis and interpretation difficult. For spatiotemporal systems, this problem is further exacerbated by their intricate dynamics. Modern machine learning methods are particular
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f96ab2dc1a2cac701ea4f6f124c95fcd
Publikováno v:
2016 41st International Conference on Infrared, Millimeter, and Terahertz waves (IRMMW-THz).
We study the dependence of extraordinary optical transmission (EOT) on the input excitation mode by placing a metal screen with a 1D array of holes inside a parallel-plate waveguide (PPWG) operating at terahertz (THz) frequencies. We demonstrate that
Publikováno v:
CLEO: 2015.
We study extraordinary optical transmission by placing a metal screen with a 1-D array of holes inside a parallel-plate waveguide at terahertz frequencies. We find excitation with TE 1 or TEM mode strongly affects output transmission characteristics.
Autor:
Belinda M. Sartor, Roman Pyrzak, Peter Y Lu, Steven N. Taylor, Richard P. Dickey, Phillip H. Rye
Publikováno v:
Fertility and Sterility. 83:671-683
To determine factors responsible for high-order multiple pregnancy (HOMP) and high-order multiple births when multiple cycles of controlled ovarian hyperstimulation-IUI (COH-IUI) are performed.Retrospective analysis.Private infertility clinic.Women (