2 citations · 3 across the 4 of their papers we have counts for
4 papers
Pseudo-Physics-Informed Neural Operators: Enhancing Operator Learning from Limited Data
Keyan Chen, Yile Li, Da Long +4
Neural operators have shown great potential in surrogate modeling. However, training a well-performing neural operator typically requires a substantial amount of data, which can po…
A Parallel-in-time Method Based on Preconditioner for Biot's Model
Zeyuan Zhou, Huipeng Gu, Guoliang Ju +1
We proposed a parallel-in-time method based on preconditioner for Biot's consolidation model in poroelasticity. In order to achieve a fast and stable convergence for the matrix sys…
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
Da Long, Wei W. Xing, Aditi S. Krishnapriyan +3
Discovering governing equations from data is important to many scientific and engineering applications. Despite promising successes, existing methods are still challenged by data s…
Multi-Resolution Active Learning of Fourier Neural Operators
Shibo Li, Xin Yu, Wei Xing +3
Fourier Neural Operator (FNO) is a popular operator learning framework. It not only achieves the state-of-the-art performance in many tasks, but also is efficient in training and p…