Learning entropy production via neural networks DK Kim, Y Bae, S Lee, H Jeong Physical Review Letters 125 (14), 140604, 2020 | 46 | 2020 |
Speed limit for a highly irreversible process and tight finite-time Landauer's bound JS Lee, S Lee, H Kwon, H Park Physical Review Letters 129 (12), 120603, 2022 | 32 | 2022 |
Finite-time quantum Otto engine: Surpassing the quasistatic efficiency due to friction S Lee, M Ha, JM Park, H Jeong Physical Review E 101 (2), 022127, 2020 | 28 | 2020 |
Inertial effects on the Brownian gyrator Y Bae, S Lee, J Kim, H Jeong Physical Review E 103 (3), 032148, 2021 | 19 | 2021 |
Quantumness and thermodynamic uncertainty relation of the finite-time Otto cycle S Lee, M Ha, H Jeong Physical Review E 103 (2), 022136, 2021 | 18 | 2021 |
Quantum mechanical bound for efficiency of quantum Otto heat engine JM Park, S Lee, HM Chun, JD Noh Physical Review E 100 (1), 012148, 2019 | 17 | 2019 |
Multidimensional entropic bound: Estimator of entropy production for Langevin dynamics with an arbitrary time-dependent protocol S Lee, DK Kim, JM Park, WK Kim, H Park, JS Lee Physical Review Research 5 (1), 013194, 2023 | 12 | 2023 |
Nonequilibrium driven by an external torque in the presence of a magnetic field S Lee, C Kwon Physical Review E 99 (5), 052142, 2019 | 8 | 2019 |
Estimating entropy production with odd-parity state variables via machine learning DK Kim, S Lee, H Jeong Physical Review Research 4 (2), 023051, 2022 | 6 | 2022 |
Estimating entanglement entropy via variational quantum circuits with classical neural networks S Lee, H Kwon, JS Lee Physical Review E 109 (4), 044117, 2024 | 2 | 2024 |
Discrete-time thermodynamic speed limit S Lee, JS Lee, JM Park arXiv preprint arXiv:2406.17966, 2024 | | 2024 |
The tightest finite-time Landauer's principle: applications of speed limit S Lee, JS Lee, H Park, H Kwon APS March Meeting Abstracts 2023, S02. 007, 2023 | | 2023 |