17 citations · 39 across the 8 of their papers we have counts for
8 papers
PDEformer-2: A Versatile Foundation Model for Two-Dimensional Partial Differential Equations
Zhanhong Ye, Zining Liu, Bingyang Wu +7
Partial differential equations (PDEs) play a central role in describing many physical phenomena. Various scientific and engineering applications demand a versatile and differentiab…
PDEformer-1: A Foundation Model for One-Dimensional Partial Differential Equations
Zhanhong Ye, Xiang Huang, Leheng Chen +5
This paper introduces PDEformer-1, a versatile neural solver capable of simultaneously addressing various partial differential equations (PDEs). With the PDE represented as a compu…
PDEformer: Towards a Foundation Model for One-Dimensional Partial Differential Equations
Zhanhong Ye, Xiang Huang, Leheng Chen +3
This paper introduces PDEformer, a neural solver for partial differential equations (PDEs) capable of simultaneously addressing various types of PDEs. We propose to represent the P…
Learning to simulate partially known spatio-temporal dynamics with trainable difference operators
Xiang Huang, Zhuoyuan Li, Hongsheng Liu +4
Recently, using neural networks to simulate spatio-temporal dynamics has received a lot of attention. However, most existing methods adopt pure data-driven black-box models, which…
Meta-Auto-Decoder: A Meta-Learning Based Reduced Order Model for Solving Parametric Partial Differential Equations
Zhanhong Ye, Xiang Huang, Hongsheng Liu +1
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, bo…
Solving Partial Differential Equations with Point Source Based on Physics-Informed Neural Networks
Xiang Huang, Hongsheng Liu, Beiji Shi +11
In recent years, deep learning technology has been used to solve partial differential equations (PDEs), among which the physics-informed neural networks (PINNs) emerges to be a pro…
Meta-Auto-Decoder for Solving Parametric Partial Differential Equations
Xiang Huang, Zhanhong Ye, Hongsheng Liu +11
Many important problems in science and engineering require solving the so-called parametric partial differential equations (PDEs), i.e., PDEs with different physical parameters, bo…
U-SLADS: Unsupervised Learning Approach for Dynamic Dendrite Sampling
Yan Zhang, Xiang Huang, Nicola Ferrier +2
Novel data acquisition schemes have been an emerging need for scanning microscopy based imaging techniques to reduce the time in data acquisition and to minimize probing radiation…