3 papers
physics.comp-ph2026
Acquiring Human-Like Data-Efficient Mechanics Prediction from Deep Reinforcement Learning
Jingruo Peng, Shuze Zhu
Humans can infer mechanical outcomes by learning from a few observations. This capacity for mechanics intuition is acquired in a data-efficient manner. Here, we propose a reinforce…
physics.comp-ph2025
Variational Learning of Physical Intuition from a Few Observations: Charting Manifolds of Variational Physics
Jingruo Peng, Shuze Zhu
Humans often generalize physical outcomes from few observations, a desirable capacity known as physical intuition. We show that it can be computationally approached through chartin…
physics.comp-ph2024
MGNN: Moment Graph Neural Network for Universal Molecular Potentials
Jian Chang, Shuze Zhu
The quest for efficient and robust deep learning models for molecular systems representation is increasingly critical in scientific exploration. The advent of message passing neura…