17 citations · 28 across the 6 of their papers we have counts for
6 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…
Analysis of the Decoder Width for Parametric Partial Differential Equations
Zhanhong Ye, Hongsheng Liu, Zidong Wang +1
Recently, Meta-Auto-Decoder (MAD) was proposed as a novel reduced order model (ROM) for solving parametric partial differential equations (PDEs), and the best possible performance…
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…
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…