273 citations · 337 across the 17 of their papers we have counts for
4 papers · 1 filter
An Unsupervised Machine Learning Approach for Ground-Motion Spectra Clustering and Selection
R. Bailey Bond, Pu Ren, Jerome F. Hajjar +1
Clustering analysis of sequence data continues to address many applications in engineering design, aided with the rapid growth of machine learning in applied science. This paper pr…
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…
Physics-informed Deep Super-resolution for Spatiotemporal Data
Pu Ren, Chengping Rao, Yang Liu +4
High-fidelity simulation of complex physical systems is exorbitantly expensive and inaccessible across spatiotemporal scales. Recently, there has been an increasing interest in lev…
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…