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Transformers for Modeling Physical Systems

4 October 2020

Papers citing "Transformers for Modeling Physical Systems"

21 / 71 papers shown

Title
Learning Flow Functions from Data with Applications to Nonlinear Oscillators Miguel Aguiar Amritam Das Karl H. Johansson 83 2 0 29 Mar 2023
Quantifying uncertainty for deep learning based forecasting and flow-reconstruction using neural architecture search ensembles R. Maulik Romain Egele Krishnan Raghavan Dali Wang UQCV AI4TS AI4CE 97 8 0 20 Feb 2023
AttNS: Attention-Inspired Numerical Solving For Limited Data Scenarios Zhongzhan Huang Mingfu Liang Liang Lin Liang Lin 150 5 0 05 Feb 2023
DLKoopman: A deep learning software package for Koopman theory Sourya Dey Eric K. Davis AI4CE 86 4 0 15 Nov 2022
Inference from Real-World Sparse Measurements Arnaud Pannatier Kyle Matoba François Fleuret AI4TS 173 0 0 20 Oct 2022
Mitigating spectral bias for the multiscale operator learning Xinliang Liu Bo Xu Shuhao Cao Lei Zhang AI4CE 221 36 0 19 Oct 2022
Efficient Learning of Mesh-Based Physical Simulation with BSMS-GNN Yadi Cao Menglei Chai Minchen Li Chenfanfu Jiang AI4CE 144 33 0 05 Oct 2022
Neural Integral Equations E. Zappala Antonio H. O. Fonseca J. O. Caro David van Dijk 100 15 0 30 Sep 2022
Physics-informed Deep Super-resolution for Spatiotemporal Data Pu Ren Chengping Rao Yang Liu Zihan Ma Qi Wang Jianxin Wang Hao Sun 130 13 0 02 Aug 2022
LordNet: An Efficient Neural Network for Learning to Solve Parametric Partial Differential Equations without Simulated Data Xinquan Huang Wenlei Shi Xiaotian Gao Xinran Wei Jia Zhang Jiang Bian Mao Yang Tie-Yan Liu PINN 101 14 0 19 Jun 2022
Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers Ran Liu Mehdi Azabou M. Dabagia Jingyun Xiao Eva L. Dyer AI4CE 127 20 0 10 Jun 2022
Transformer for Partial Differential Equations' Operator Learning Zijie Li Kazem Meidani A. Farimani 165 192 0 26 May 2022
Predicting Physics in Mesh-reduced Space with Temporal Attention Xu Han Han Gao Tobias Pfaff Jian-Xun Wang Liping Liu AI4CE 157 88 0 22 Jan 2022
GrADE: A graph based data-driven solver for time-dependent nonlinear partial differential equations Y. Kumar S. Chakraborty 82 8 0 24 Aug 2021
Is attention to bounding boxes all you need for pedestrian action prediction? Lina Achaji Julien Moreau Thibault Fouqueray François Aioun François Charpillet 113 38 0 16 Jul 2021
PhyCRNet: Physics-informed Convolutional-Recurrent Network for Solving Spatiotemporal PDEs Pu Ren Chengping Rao Yang Liu Jianxun Wang Hao Sun DiffM AI4CE 172 219 0 26 Jun 2021
Learning effective stochastic differential equations from microscopic simulations: linking stochastic numerics to deep learning Felix Dietrich Alexei Makeev George A. Kevrekidis N. Evangelou Tom S. Bertalan Sebastian Reich Ioannis G. Kevrekidis DiffM 146 45 0 10 Jun 2021
Encoding physics to learn reaction-diffusion processes Chengping Rao Pu Ren Qi Wang O. Buyukozturk Haoqin Sun Yang Liu PINN AI4CE DiffM 166 110 0 09 Jun 2021
CKNet: A Convolutional Neural Network Based on Koopman Operator for Modeling Latent Dynamics from Pixels Yongqian Xiao Xin Xu Yifei Shi 71 9 0 19 Feb 2021
State estimation with limited sensors -- A deep learning based approach Y. Kumar Pranav Bahl S. Chakraborty 74 30 0 27 Jan 2021
A deep learning modeling framework to capture mixing patterns in reactive-transport systems N. V. Jagtap M. Mudunuru K. Nakshatrala 50 5 0 11 Jan 2021