Hello! I am Miao Chen, a graduate student in physics at Beijing Normal University. I am pursuing an M.Sc. in Theoretical Physics at BNU, following my B.Sc. in Physics from Jiangxi Normal University in 2024. My research focuses on non-equilibrium statistical physics, with particular interests in dynamic hysteresis, finite-time and finite-size scaling, and information thermodynamics.

I am interested in how driven many-body systems respond across different time and length scales, and how universal constraints emerge from microscopic dynamics. Recent projects include coercivity landscapes in dynamic hysteresis and finite-time thermodynamics of autonomous information machines.

  • How can we organize complex hysteresis dynamics into a unified scaling landscape?
  • What universal behavior appears when finite driving time and finite system size compete?
  • How do information, dissipation, and thermodynamic performance constrain small engines?
If my research resonates with you, please feel free to contact me by email.

News

  • 2026.04Our preprint on finite-time thermodynamics of an autonomous information machine is available on arXiv.
  • 2026.03Our two companion papers on dynamic hysteresis and coercivity landscapes were published back-to-back in Physical Review Letters and Physical Review E. The PRL paper was selected as an Editors’ Suggestion.
  • 2024.03Our paper on Rényi entanglement asymmetry in conformal field theories appeared in Physical Review D.

Selected Publications

Dynamic hysteresis and coercivity landscapes
Coercivity landscape figure

Coercivity Landscape Characterizes Dynamic Hysteresis

Miao Chen*, Xiu-Hua Zhao*, and Yu-Han Ma

Physical Review Letters 136, 117102 (2026). Editors’ Suggestion.

A unified landscape picture for dynamic hysteresis across slow-to-fast driving regimes.

Editors’ SuggestionDynamic hysteresisCoercivity landscape
Information thermodynamics
Quantum many-body theory

CV

A PDF version of my academic CV is available here: Download CV.

Research Interests

Non-equilibrium statistical physics; phase transitions; information thermodynamics.

Education

Sep. 2024present
M.Sc. in Theoretical Physics
Beijing Normal University, Beijing, China
Advisor: Prof. Yu-Han Ma · GPA: 3.8/4
Sep. 2020Jun. 2024
B.Sc. in Physics
Jiangxi Normal University, Nanchang, China
Advisor: Prof. Hui-Huang Chen · GPA: 90/100 · Honor: Outstanding Graduate

Publications

  1. Miao Chen*, Xiu-Hua Zhao*, and Yu-Han Ma, “Coercivity Landscape Characterizes Dynamic Hysteresis,” Physical Review Letters 136, 117102 (2026). Editors’ Suggestion.
  2. Miao Chen, Xiu-Hua Zhao, and Yu-Han Ma, “Finite-time and finite-size scalings of coercivity in dynamic hysteresis,” Physical Review E 113, 034124 (2026).
  3. Wanyan Chen*, Miao Chen*, and Yu-Han Ma, “Finite-Time Thermodynamics of an Autonomous Information Machine,” arXiv:2604.15953 (2026).
  4. Miao Chen and Hui-Huang Chen, “Rényi entanglement asymmetry in (1+1)-dimensional conformal field theories,” Physical Review D 109, 065009 (2024).
  5. Miao Chen and Qin Guo, “Theoretical Study and Visualization of the Winding Problem,” College Physics 43(4), 65–68, 80 (2024). [in Chinese]

* denotes equal contribution.

Research Experience

School of Physics and Astronomy, Beijing Normal University
Dec. 2023–present
Advisor: Prof. Yu-Han Ma
  • Developed a coercivity-landscape framework to characterize finite-time and finite-size scaling behaviors in dynamic hysteresis using renormalization techniques.
  • Performed kinetic Monte Carlo simulations of the two-dimensional Ising model in driven systems.
  • Studied finite-time thermodynamics and performance trade-offs in information machines.
Department of Physics, Jiangxi Normal University
Feb. 2023–Jun. 2024
Advisor: Prof. Hui-Huang Chen
  • Investigated symmetry breaking and entanglement-related quantities in conformal field theories.
  • Implemented numerical tests of CFT predictions using exactly solvable lattice models.

Teaching Experience

  • Teaching Assistant, University Physics at BNU, 2025.
  • Teaching Assistant, Advanced Mathematics at JXNU, 2021–2023.

Conferences

  • International Conference on Statistical Physics (29th), Florence, Italy.
    Poster: Coercivity Panorama of Dynamic Hysteresis. Jul. 13–18, 2025.
  • Progress in Quantum Machine Learning (2026), Beijing, China.
    Poster: Coercivity Landscape Characterizes Dynamic Hysteresis. May 10, 2026.
  • National Conference on Magnetics Theory (20th), Hangzhou, China.
    Poster: Coercivity Landscape Characterizes Dynamic Hysteresis. May 22–25, 2026.