11 citations · 12 across the 4 of their papers we have counts for
8 papers
Poisson Flow Generative Models
Yilun Xu, Ziming Liu, Max Tegmark +1
We propose a new "Poisson flow" generative model (PFGM) that maps a uniform distribution on a high-dimensional hemisphere into any data distribution. We interpret the data points a…
Physics-Augmented Learning: A New Paradigm Beyond Physics-Informed Learning
Ziming Liu, Yunyue Chen, Yuanqi Du +1
Integrating physical inductive biases into machine learning can improve model generalizability. We generalize the successful paradigm of physics-informed learning (PIL) into a more…
AI Poincaré: Machine Learning Conservation Laws from Trajectories
Ziming Liu, Max Tegmark
We present AI Poincaré, a machine learning algorithm for auto-discovering conserved quantities using trajectory data from unknown dynamical systems. We test it on five Hamiltonian…
Robustness of principal component analysis on harmonic flow in heavy ion collisions
Ziming Liu, Arabinda Behera, Huichao Song +1
The principal component analysis (PCA), a mathematical tool commonly used in statistics, has recently been employed to interpret the -dependent fluctuations of harmonic flow $…
Influenza Modeling Based on Massive Feature Engineering and International Flow Deconvolution
Ziming Liu, Yixuan Wang, Zizhao Han +1
In this article, we focus on the analysis of the potential factors driving the spread of influenza, and possible policies to mitigate the adverse effects of the disease. To be prec…
Quantum-Inspired Hamiltonian Monte Carlo for Bayesian Sampling
Ziming Liu, Zheng Zhang
Hamiltonian Monte Carlo (HMC) is an efficient Bayesian sampling method that can make distant proposals in the parameter space by simulating a Hamiltonian dynamical system. Despite…