Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Ehsan Khatami"'
Autor:
Xiqiao Wang, Ehsan Khatami, Fan Fei, Jonathan Wyrick, Pradeep Namboodiri, Ranjit Kashid, Albert F. Rigosi, Garnett Bryant, Richard Silver
Publikováno v:
Nature Communications, Vol 13, Iss 1, Pp 1-12 (2022)
Atomically precise artificial lattices of dopant-based quantum dots offer a tunable platform for simulations of interacting fermionic models. By leveraging advances in fabrication and atomic-state control, Wang et al. report quantum simulations of th
Externí odkaz:
https://doaj.org/article/b1c2a41d4c9c4435bc1e4b3deb2038b6
Publikováno v:
Carbon Trends, Vol 9, Iss , Pp 100231- (2022)
Numerical approaches to the correlated electron problem have achieved considerable success, yet are still constrained by several bottlenecks, including high order polynomial or exponential scaling in system size, long autocorrelation times, challenge
Externí odkaz:
https://doaj.org/article/b1f5c75979204ff6acbfe4b6afff0cc1
Publikováno v:
Physical Review X, Vol 7, Iss 3, p 031038 (2017)
Machine learning offers an unprecedented perspective for the problem of classifying phases in condensed matter physics. We employ neural-network machine learning techniques to distinguish finite-temperature phases of the strongly correlated fermions
Externí odkaz:
https://doaj.org/article/370dd3016451439d8d6cda3ffe58f45c
Publikováno v:
Journal of Theoretical and Computational Acoustics. 30
Acoustic metamaterials are engineered microstructures with special mechanical and acoustic properties enabling exotic effects such as wave steering, focusing and cloaking. In this research, we develop a new machine learning framework for predicting o
Autor:
Jacob Park, Ehsan Khatami
Publikováno v:
Physical Review B. 104
The interplay of disorder and strong correlations in quantum many-body systems remains an open question. That is despite much progress made in recent years with ultracold atoms in optical lattices to better understand phenomena such as many-body loca
Autor:
Kelvin Ch'ng, Kazuhiro Fujita, Hiroshi Eisaki, Andrej Mesaros, J. C. Séamus Davis, S. Uchida, Stephen D. Edkins, Yi Zhang, Eun-Ah Kim, Mohammad Hamidian, Ehsan Khatami
Publikováno v:
Nature
Essentials of the scientific discovery process have remained largely unchanged for centuries: systematic human observation of natural phenomena is used to form hypotheses that, when validated through experimentation, are generalized into established
Publikováno v:
Physical Review B
Physical Review B, American Physical Society, 2021, 103 (6), ⟨10.1103/PhysRevB.103.L060501⟩
Physical Review B, American Physical Society, 2021, 103 (6), ⟨10.1103/PhysRevB.103.L060501⟩
The interplay between electron-electron correlations and disorder has been a central theme of condensed matter physics over the past several decades, with particular interest in the possibility that interactions might cause delocalization of an Ander
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::22a19958a2ba722b0e0124ad5f21b5da
https://hal.archives-ouvertes.fr/hal-03261765
https://hal.archives-ouvertes.fr/hal-03261765
Publikováno v:
The Journal of the Acoustical Society of America. 150:A209-A209
This study presents a promising data-driven approach for approximating and minimizing acoustic wave scattering in 2D acoustic cloak design, in which the optimal arrangement of cylindrical structures is found via a combination of generative deep learn
Autor:
Feruza Amirkulova, Wei-Ching Wang, Yanru Chen, Ehsan Khatami, Grace Kwak, Don Robert L. Pornaras
Publikováno v:
The Journal of the Acoustical Society of America. 146:2876-2877
We will demonstrate a novel method to simulate acoustic multiple scattering by a configuration of cylinders and solve inverse problems using artificial neural networks (NN) and deep learning. We will research how to apply deep learning to solve inver