4 citations · 9 across the 3 of their papers we have counts for
3 papers
physics.geo-ph2024★ 3 cited
Learning Physics for Unveiling Hidden Earthquake Ground Motions via Conditional Generative Modeling
Pu Ren, Rie Nakata, Maxime Lacour +9
Predicting high-fidelity ground motions for future earthquakes is crucial for seismic hazard assessment and infrastructure resilience. Conventional empirical simulations suffer fro…
cs.LG2024★ 4 cited
Data-Efficient Operator Learning via Unsupervised Pretraining and In-Context Learning
Wuyang Chen, Jialin Song, Pu Ren +3
Recent years have witnessed the promise of coupling machine learning methods and physical domain-specific insights for solving scientific problems based on partial differential equ…
cs.LG2023★ 2 cited
Generative Modeling of Regular and Irregular Time Series Data via Koopman VAEs
Ilan Naiman, N. Benjamin Erichson, Pu Ren +2
Generating realistic time series data is important for many engineering and scientific applications. Existing work tackles this problem using generative adversarial networks (GANs)…