Articoli con mandati relativi all'accesso pubblico - Samuel KimUlteriori informazioni
Non disponibile pubblicamente: 1
Thermally tunable infrared metasurfaces
D Shrekenhamer, K S. J., C L. J., LB Ruppalt, JG Champlain, ...
Eleventh International Congress on Engineered Material Platforms for Novel …, 2017
Mandati: US Department of Defense
Disponibili pubblicamente: 9
Integration of Neural Network-Based Symbolic Regression in Deep Learning for Scientific Discovery
S Kim, PY Lu, S Mukherjee, M Gilbert, L Jing, V Čeperić, M Soljačić
IEEE Transactions on Neural Networks and Learning Systems, 2020
Mandati: US Department of Defense
Enhanced Strain Coupling of Nitrogen-Vacancy Spins to Nanoscale Diamond Cantilevers
S Meesala, YI Sohn, HA Atikian, S Kim, MJ Burek, JT Choy, M Lončar
Phys. Rev. Applied 5 (3), 2016
Mandati: US National Science Foundation, Natural Sciences and Engineering Research …
Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning
PY Lu, S Kim, M Soljačić
Physical Review X 10 (3), 031056, 2020
Mandati: US Department of Defense
Deep learning and symbolic regression for discovering parametric equations
M Zhang, S Kim, PY Lu, M Soljačić
IEEE Transactions on Neural Networks and Learning Systems, 2023
Mandati: US National Science Foundation, US Department of Defense
Surrogate-and invariance-boosted contrastive learning for data-scarce applications in science
C Loh, T Christensen, R Dangovski, S Kim, M Soljačić
Nature Communications 13 (1), 4223, 2022
Mandati: US National Science Foundation, US Department of Defense
Automated discovery and optimization of 3D topological photonic crystals
S Kim, T Christensen, SG Johnson, M Soljacic
ACS Photonics 10 (4), 861-874, 2023
Mandati: US Department of Defense
Hydrogen diffusion behavior and vacancy interaction behavior in OsO2 and RuO2 by ab initio calculations
S Kim, W Lai
Computational Materials Science 102, 14-20, 2015
Mandati: National Natural Science Foundation of China
Interpretable Neuroevolutionary Models for Learning Non-Differentiable Functions and Programs
A Costa, R Dangovski, S Kim, P Goyal, M Soljačić, J Jacobson
arXiv preprint arXiv:2007.10784, 2020
Mandati: US Department of Defense
Deep Learning and Symbolic Regression for Discovering Parametric Equations
S Kim, M Zhang, PY Lu, M Soljacic
ICML 2022 2nd AI for Science Workshop, 2022
Mandati: US National Science Foundation, US Department of Defense
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