Quantum dynamics
Neural-network approaches to the time-dependent Schrödinger equation and quantum many-body dynamics.
Computational physics & machine learning
Enze Hou
Postdoctoral researcher SUTD
I study machine learning methods for multi-scale modeling, with a focus on quantum many-body physics and ultrafast dynamics.
I am a postdoctoral researcher at the Singapore University of Technology and Design (SUTD). I received my PhD in Fundamental Mathematics in June 2026 from the Institute of Applied Physics and Computational Mathematics, advised by Prof. Han Wang at the National Key Laboratory of Computational Physics.
Research interests
Methods & applicationsMachine learning potentials
Atomistic modeling of the dynamic mechanical response of materials under extreme conditions.
Scientific computing
Multi-scale modeling, Monte Carlo methods, and uncertainty quantification.
Recent publications
All publicationsJournal on Numerical Methods and Computer Applications 47(2), 160–183
Machine Learning-Based Methods for Electronic Structure Calculations
Machine Learning: Science and Technology 7(3), 035012
A global spacetime optimization approach to the real-space time-dependent Schrödinger equation
Chinese Journal of High Pressure Physics 40(1), 010106
A Machine Learning Potential Model for Simulating Dynamic Mechanical Response of Pb-Sn Alloy
* Corresponding author.