Samuel Kim
Cited by
Cited by
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
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
Tough iron-based bulk metallic glass alloys
ST Kim, MD Demetriou, WL Johnson
US Patent 8,911,572, 2014
Extracting interpretable physical parameters from spatiotemporal systems using unsupervised learning
PY Lu, S Kim, M Soljačić
Physical Review X 10 (3), 031056, 2020
Deep Learning for Bayesian Optimization of Scientific Problems with High-Dimensional Structure
S Kim, PY Lu, C Loh, J Smith, J Snoek, M Soljačić
arXiv e-prints, arXiv: 2104.11667, 2021
In-Situ Monitoring and Modeling of Metal Additive Manufacturing Powder Bed Fusion
J Alldredge, J Soltwinski, S Storck, S Kim, A Goldberg
Review of Progress in Quantitative Nondestructive Evaluation 1949 (1), 020007, 2018
Luneburg Lens for Wide-Angle Chip-Scale Optical Beam Steering
S Kim, J Sloan, JJ Lˇpez, D Kharas, J Herd, S Bramhavar, P Juodawlkis, ...
CLEO: Science and Innovations, SF3N. 7, 2019
Tunable Superconducting Cavity using Superconducting Quantum Interference Device Metamaterials
S Kim, D Shrekenhamer, K McElroy, A Strikwerda, J Alldredge
Scientific reports 9 (1), 1-9, 2019
High impedance holographic metasurfaces for conformal and high gain antenna applications
S Kim, D Shrekenhamer, J Will, R Awadallah, J Miragliotta
2018 15th IEEE Annual Consumer Communications & Networking Conference (CCNC), 2018
Fast Neural Models for Symbolic Regression at Scale
A Costa, R Dangovski, O Dugan, S Kim, P Goyal, M Soljačić, J Jacobson
arXiv preprint arXiv:2007.10784, 2020
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
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
Deep Learning and Symbolic Regression for Discovering Parametric Equations
M Zhang, S Kim, PY Lu, M Soljačić
arXiv preprint arXiv:2207.00529, 2022
Strain coupling of diamond nitrogen vacancy centers to nanomechanical resonators
S Meesala, YI Sohn, HA Atikian, MJ Burek, S Kim, J Choy, M Loncar
CLEO: QELS_Fundamental Science, FTh3B. 4, 2015
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), 1-12, 2022
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
Deep Learning for Bayesian Optimization of High-Dimensional Scientific Problems
S Kim, P Lu, C Loh, M Soljačić, J Snoek, J Smith
Bulletin of the American Physical Society, 2022
Design of a photonic crystal planar Luneburg lens for optical beam steering
S Kim
Massachusetts Institute of Technology, 2019
Thermally tunable infrared metasurfaces
D Shrekenhamer, K S. J., C L. J., LB Ruppalt, JG Champlain, ...
Eleventh International Congress on Engineered Material Platforms for Novelá…, 2017
Extracting Interpretable Physical Parameters from Partial Differential Equations using Unsupervised Learning
PY Lu, S Kim, M Soljacic
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