View count:
273
Program
| 2026/1/12 NCTS 2026 Frontiers of Complex Systems Winter School @ CCU | ||
| Time | Speaker | Title |
| 09:30-10:00 | Registration & opening | |
| 10:00-11:00 | Tan Van Vu | Lecture 1: Thermodynamic uncertainty relations |
| 11:00-11:20 | Coffee Break | |
| 11:20-12:20 | Junghyo Jo | Lecture 1. Generative Models |
| 12:20-14:00 | Lunch | |
| 14:00-15:00 | Tan Van Vu | Lecture 2: Optimal transport theory and thermodynamic speed limits |
| 15:00-15:20 | Coffee Break | |
| 15:20-16:20 | Junghyo Jo | Lecture 2. Network Inference |
| 16:20-17:00 | Free discussion & closing | |
Thermodynamics of Precision and Speed
Prof. Tan Van Vu (Kyoto University)
Lecture 1: Thermodynamic uncertainty relations
1.1. Overview of stochastic thermodynamics
1.2. Thermodynamic uncertainty relations
1.3. Applications
Lecture 2: Optimal transport theory and thermodynamic speed limits
2.1. Optimal transport theory
2.2. Thermodynamic speed limits
2.3. Applications
References
[1] Y. Hasegawa and T. Van Vu, Fluctuation theorem uncertainty relation, Physical Review Letters 123, 110602 (2019)
[2] T. Van Vu and K. Saito, Thermodynamics of precision in Markovian open quantum dynamics, Physical Review Letters 128, 140602 (2022)
[3] T. Van Vu and K. Saito, Thermodynamic unification of optimal transport: Thermodynamic uncertainty relation, minimum dissipation, and thermodynamic speed limits, Physical Review X 13, 011013 (2023)
[4] T. Van Vu and K. Saito, Topological speed limit, Physical Review Letters 130, 010402 (2023)
[5] T. Van Vu, Fundamental bounds on precision and response for quantum trajectory observables, PRX Quantum 6, 010343 (2025)
Data Science with Machine Learning
Prof. Junghyo Jo (Seoul National University)
Lecture 1. Generative Models
1.1 Energy-based models: Hopfield networks and Boltzmann machines
1.2 Latent-variable generative models: Variational Autoencoders (VAEs)
1.3 Score-based generative modeling: Diffusion models
Lecture 2. Network Inference
2.1 Maximum likelihood estimation for network reconstruction
2.2 Erasure Machine: principles and algorithms for inference
2.3 Diffusion-based data augmentation for robust network inference
References
[1] Data augmentation using diffusion models to enhance inverse Ising inference
Y Lim, S Lee, J Jo
Physical Review E 111 (4), 045302 (2025)
[2] Erasure machine: Inverse Ising inference from reweighting of observation frequencies
J Jo, DT Hoang, V Periwal
Physical Review E 101 (3), 032107 (2020)
