Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs

18 December 2023

Papers citing "Inducing Point Operator Transformer: A Flexible and Scalable Architecture for Solving PDEs"

8 / 8 papers shown

Title
SCENT: Robust Spatiotemporal Learning for Continuous Scientific Data via Scalable Conditioned Neural Fields David K. Park Xihaier Luo Guang Zhao Seungjun Lee M. Oprescu Shinjae Yoo AI4TS 19 0 0 16 Apr 2025
GeoAggregator: An Efficient Transformer Model for Geo-Spatial Tabular Data Rui Deng Ziqi Li Mingshu Wang 33 0 0 24 Feb 2025
Physics-Informed Geometry-Aware Neural Operator Weiheng Zhong Hadi Meidani AI4CE 30 4 0 02 Aug 2024
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries Zong-Yi Li Daniel Zhengyu Huang Burigede Liu Anima Anandkumar AI4CE 108 246 0 11 Jul 2022
Multiwavelet-based Operator Learning for Differential Equations Gaurav Gupta Xiongye Xiao P. Bogdan 124 200 0 28 Sep 2021
Fourier Neural Operator for Parametric Partial Differential Equations Zong-Yi Li Nikola B. Kovachki Kamyar Azizzadenesheli Burigede Liu K. Bhattacharya Andrew M. Stuart Anima Anandkumar AI4CE 203 2,272 0 18 Oct 2020
MeshfreeFlowNet: A Physics-Constrained Deep Continuous Space-Time Super-Resolution Framework C. Jiang S. Esmaeilzadeh Kamyar Azizzadenesheli K. Kashinath Mustafa A. Mustafa H. Tchelepi P. Marcus P. Prabhat Anima Anandkumar AI4CE 182 140 0 01 May 2020
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation C. Qi Hao Su Kaichun Mo Leonidas J. Guibas 3DH 3DPC 3DV PINN 219 14,047 0 02 Dec 2016