613 citations · 1k across the 21 of their papers we have counts for
21 papers
RSRM: Reinforcement Symbolic Regression Machine
Yilong Xu, Yang Liu, Hao Sun
In nature, the behaviors of many complex systems can be described by parsimonious math equations. Automatically distilling these equations from limited data is cast as a symbolic r…
Physics-informed neural network for seismic wave inversion in layered semi-infinite domain
Pu Ren, Chengping Rao, Hao Sun +1
Estimating the material distribution of Earth's subsurface is a challenging task in seismology and earthquake engineering. The recent development of physics-informed neural network…
SeismicNet: Physics-informed neural networks for seismic wave modeling in semi-infinite domain
Pu Ren, Chengping Rao, Su Chen +3
There has been an increasing interest in integrating physics knowledge and machine learning for modeling dynamical systems. However, very limited studies have been conducted on sei…
Symbolic Physics Learner: Discovering governing equations via Monte Carlo tree search
Fangzheng Sun, Yang Liu, Jian-Xun Wang +1
Nonlinear dynamics is ubiquitous in nature and commonly seen in various science and engineering disciplines. Distilling analytical expressions that govern nonlinear dynamics from l…
Discovering Nonlinear PDEs from Scarce Data with Physics-encoded Learning
Chengping Rao, Pu Ren, Yang Liu +1
There have been growing interests in leveraging experimental measurements to discover the underlying partial differential equations (PDEs) that govern complex physical phenomena. A…
Effective and efficient structure learning with pruning and model averaging strategies
Anthony C. Constantinou, Yang Liu, Neville K. Kitson +2
Learning the structure of a Bayesian Network (BN) with score-based solutions involves exploring the search space of possible graphs and moving towards the graph that maximises a gi…