Ziming Liu (刘子鸣)

PhD student


Biography CV

I am a first-year Physics PhD student at MIT and IAIFI, advised by Prof. Max Tegmark. I have interned at Microsoft Research Asia. Before that, I received my Bachelor's degree in physics from Peking University. Prior to that, my memories are sealed in my hometown, Wuhan.

I research on the intersection of artificial intelligence and physics in general. My research interests include: (1) AI for physics: extracting physical insights (e.g. conservation laws and symmetries) from data, improving prediction accuracy and sampling efficiency for data analysis in physics; (2) Physics for AI: developing effective theories to understand the dynamics and generalization of neural networks. Besides that, I am generally interested in applications of AI to chemistry, biology, vision, language, medicine and robotics. I am happy and open to any form of collaboration.

I love music and mobile photography in my leisure time, but also have great fun thinking about (somewhat philosophical) questions regarding the boundary (if any) between human & artificial intelligence, mathematical modeling of humanities and other nerdy stuff.

Recent Publications

Physics-augmented Learning: A new paradigm beyond physics-informed learning (Ziming Liu, Yunyue Chen, Yuanqi Du and Max Tegmark)
Comment: we propose a learning framework which unifies the already successful physics-informed learning paradigm and a novel paradigm called physics-augmented learning.


Machine Learning Hidden Symmetries (Ziming Liu and Max Tegmark)
Comment: We present a method searching for hidden symmetries revealed by coordinate transformations parameterized by neural networks.


Machine-Learning Non-Conservative Dynamics for New-Physics Detection (Ziming Liu, Bohan Wang, Meng Qi, Wei Chen, Max Tegmark and Tie-Yan Liu)
Comment: We present Neural New-Physics Detector (NNPhD), a machine learning algorithm for decomposing conservative and non-conservative forces. NNPhD is a natural extension of Lagrangian Neural Network.

arxiv PRE code

AI Poincaré: Machine Learning Conservation Laws from Trajectories. (Ziming Liu and Max Tegmark)
Comment: We present AI Poincaré, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We released our code on PyPI here, and you could simply install aipoincare package by typing in pip install aipoincare.

arxiv PRL PyPI code github code

Schrodinger PCA: You only Need Variances for Eigenmodes (Ziming Liu, Sitian Qian, Yixuan Wang, Yuxuan Yan and Tianyi Yang)
Comment: We make an intriguing connection between quantum mechanics and principal component analysis.

arxiv PRE github code youtube video

Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling (Ziming Liu and Zheng Zhang)
Comment: What will happen when quantum mechanics meets hamiltonian monte carlo? The quantum mass achieves better sampling results on spiky and multi-modal distributions.

arxiv github code

Random Research Topics

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