Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses | Paper Citation Record

Observation 19c0b453-3f61-4e71-8727-2dde493ffecb · outbound

This paper cites Pantelis Elinas and Edwin V.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Pantelis Elinas and Edwin V

Reference 9

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This paper cites MultiScale MeshGraphNets.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses MultiScale MeshGraphNets

Reference 11

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This paper cites Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Nikola Kovachki, Zongyi Li, Burigede Liu, Kamyar Azizzadenesheli, Kaushik Bhattacharya, Andrew Stuart, and Anima Anandkumar

Reference 14

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This paper cites SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts

Reference 17

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This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 18

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This paper cites Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising

Reference 23

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Observation c8c3f3a8-f199-4a6e-b6f2-e829e84b7cb5 · outbound

This paper cites Learning Mesh-Based Simulation with Graph Networks.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Learning Mesh-Based Simulation with Graph Networks

Reference 26

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This paper cites GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

Reference 27

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Observation 79f7ce09-512d-430e-bf36-02c113891c1d · outbound

This paper cites GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses GNN-Surrogate: A Hierarchical and Adaptive Graph Neural Network for Parameter Space Exploration of Unstructured-Mesh Ocean Simulations

Reference 28

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Observation c5fdf6a1-1ffa-4e91-b997-5c9e684745c2 · outbound

This paper cites Ljubisa Stankovic, Jonatan Lerga, Danilo P.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Ljubisa Stankovic, Jonatan Lerga, Danilo P

Reference 30

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Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Unresolved cited work

Reference 31

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Observation 50b914d2-3c31-4787-9351-48820c7b2539 · outbound

This paper cites Alexander Tong, David van Dijk, Jay S.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Alexander Tong, David van Dijk, Jay S

Reference 32

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Observation 423c96c8-d55f-477a-946a-bac29403f111 · outbound

This paper cites GRASPEL: Graph Spectral Learning at Scale.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses GRASPEL: Graph Spectral Learning at Scale

Reference 33

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Observation 4272249a-287a-4da5-befb-b0674bbe2f00 · outbound

This paper cites GRASPEL: Graph Spectral Learning at Scale.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses GRASPEL: Graph Spectral Learning at Scale

Reference 34

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This paper cites Recurrent Neural Operators: Stable Long-Term PDE Prediction.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Recurrent Neural Operators: Stable Long-Term PDE Prediction

Reference 35

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This paper cites Xiaotong Zhang, Han Liu, Xiao-Ming Wu, Xianchao Zhang, and Xinyue Liu.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Xiaotong Zhang, Han Liu, Xiao-Ming Wu, Xianchao Zhang, and Xinyue Liu

Reference 36

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This paper cites an unresolved cited work.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Unresolved cited work

Reference 37

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Observation df0db161-ddfc-42da-ab39-fb1313676aef · outbound

This paper cites Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Michaël Defferrard, Xavier Bresson, and Pierre Vandergheynst

Reference 1970

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Observation 739edc17-a07a-4e15-b910-b32c6cb93124 · outbound

This paper cites William L.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses William L

Reference 1977

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This paper cites Yadi Cao, Menglei Chai, Minchen Li, and Chenfanfu Jiang.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Yadi Cao, Menglei Chai, Minchen Li, and Chenfanfu Jiang

Reference 1982

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This paper cites Michael McCabe, Peter Harrington, Shashank Subramanian, and Jed Brown.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Michael McCabe, Peter Harrington, Shashank Subramanian, and Jed Brown

Reference 1997

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This paper cites Alexander N.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Alexander N

Reference 2000

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This paper cites Yinan Huang, Wei Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, and Pan Li.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Yinan Huang, Wei Lu, Joshua Robinson, Yu Yang, Muhan Zhang, Stefanie Jegelka, and Pan Li

Reference 2011

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Observation 262b2134-4567-474c-9667-c7789445f9fb · outbound

This paper cites Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Perturbation of the Eigenvectors of the Graph Laplacian: Application to Image Denoising

Reference 2012

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Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Unresolved cited work

Reference 2013

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This paper cites SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses SGNO: Spectral Generator Neural Operators for Stable Long Horizon PDE Rollouts

Reference 2015

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This paper cites DSO: Dual-scale neural operators for stable long-term fluid dynamics forecasting.arXiv preprint arXiv:2603.26800,.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses DSO: Dual-scale neural operators for stable long-term fluid dynamics forecasting.arXiv preprint arXiv:2603.26800,

Reference 2016

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This paper cites Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI

Reference 2017

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Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Unresolved cited work

Reference 2018

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This paper cites Revisiting Graph Neural Networks: All We Have is Low-Pass Filters.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Revisiting Graph Neural Networks: All We Have is Low-Pass Filters

Reference 2019

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This paper cites Neural Operator: Graph Kernel Network for Partial Differential Equations.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 2020

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This paper cites Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Python Non-Uniform Fast Fourier Transform (PyNUFFT): multi-dimensional non-Cartesian image reconstruction package for heterogeneous platforms and applications to MRI

Reference 2021

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This paper cites Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Addressing Over-Smoothing in Graph Neural Networks via Deep Supervision

Reference 2022

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This paper cites Hojin Kim, Varun Shankar, Venkatasubramanian Viswanathan, and Romit Maulik.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Hojin Kim, Varun Shankar, Venkatasubramanian Viswanathan, and Romit Maulik

Reference 2023

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This paper cites 2024.102335.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses 2024.102335

Reference 2024

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Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses Unresolved cited work

Reference 2025

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This paper cites 38 De Cheng, Yihong Gong, Xiaojun Chang, Weiwei Shi, Alexander G.

Scale-Aware Learning of Chaotic Dynamics on Unstructured Meshes via Binned Spectral Losses 38 De Cheng, Yihong Gong, Xiaojun Chang, Weiwei Shi, Alexander G

Reference 2026

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