115 citations · 231 across the 17 of their papers we have counts for
4 papers · 1 filter
Learning Transition Operators From Sparse Space-Time Samples
Christian Kümmerle, Mauro Maggioni, Sui Tang
We consider the nonlinear inverse problem of learning a transition operator from partial observations at different times, in particular from sparse observations of ent…
An Interpretable Hybrid Predictive Model of COVID-19 Cases using Autoregressive Model and LSTM
Yangyi Zhang, Sui Tang, Guo Yu
The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for…
Higher-Order error estimates for physics-informed neural networks approximating the primitive equations
Ruimeng Hu, Quyuan Lin, Alan Raydan +1
Large-scale dynamics of the oceans and the atmosphere are governed by primitive equations (PEs). Due to the nonlinearity and nonlocality, the numerical study of the PEs is generall…
Scalable marginalization of correlated latent variables with applications to learning particle interaction kernels
Mengyang Gu, Xubo Liu, Xinyi Fang +1
Marginalization of latent variables or nuisance parameters is a fundamental aspect of Bayesian inference and uncertainty quantification. In this work, we focus on scalable marginal…