Learning Mappings in Mesh-based Simulations | Paper Citation Record

Observation b0799e91-e7af-4f24-8b3e-5419b28d6095 · outbound

This paper cites Convolution Optimization, pages 85–126.

Learning Mappings in Mesh-based Simulations Convolution Optimization, pages 85–126

Reference 1

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.771059Z

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Observation 383e19f6-77ec-42af-bf63-6d102fca7fea · outbound

This paper cites Convolutional neural networks for steady flow approxi- mation.

Learning Mappings in Mesh-based Simulations Convolutional neural networks for steady flow approxi- mation

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.758010Z

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Observation 6d2c4d00-c1be-4ba3-9aaf-757705f0e640 · outbound

This paper cites Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes.

Learning Mappings in Mesh-based Simulations Deep Learning for Real-Time Aerodynamic Evaluations of Arbitrary Vehicle Shapes

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.736692Z

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Observation bcd2769d-e0b2-4e14-856c-e6ad6848d1fc · outbound

This paper cites U-net: Convolutional networks for biomedical image segmentation.

Learning Mappings in Mesh-based Simulations U-net: Convolutional networks for biomedical image segmentation

Reference 4

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Observation 5b376edc-362c-45f5-8cb8-41ab421fdbee · outbound

This paper cites Surfnet: Super-resolution of turbulent flows with transfer learning using small datasets.

Learning Mappings in Mesh-based Simulations Surfnet: Super-resolution of turbulent flows with transfer learning using small datasets

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.735677Z

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Observation 3e588215-0720-4901-95ef-1572743e25b9 · outbound

This paper cites Computa- tionally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network.

Learning Mappings in Mesh-based Simulations Computa- tionally effective estimation of supersonic flow field around airfoils using sparse convolutional neural network

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.723068Z

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Observation 5192e918-a6cd-48d4-9f6c-34f440d50cf8 · outbound

This paper cites Sparse convo- lutional neural networks.

Learning Mappings in Mesh-based Simulations Sparse convo- lutional neural networks

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.709696Z

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Observation 25353038-e8cd-4305-9a16-abb321c154e4 · outbound

This paper cites UCNN: A Convolutional Strategy on Unstructured Mesh.

Learning Mappings in Mesh-based Simulations UCNN: A Convolutional Strategy on Unstructured Mesh

Reference 8

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Observation c980cff3-6d86-46a6-97d7-59ae448ad892 · outbound

This paper cites Deep parametric continuous convolutional neural networks.

Learning Mappings in Mesh-based Simulations Deep parametric continuous convolutional neural networks

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.696335Z

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Observation a88dd78b-e3f7-44b7-8faa-59ff130b042f · outbound

This paper cites U-net-based surrogate model for evaluation of microfluidic channels.

Learning Mappings in Mesh-based Simulations U-net-based surrogate model for evaluation of microfluidic channels

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.683588Z

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Observation 1919c1b9-0778-4ae7-902f-168d9546405a · outbound

This paper cites A finite element-convolutional neural network model (fe- cnn) for stress field analysis around arbitrary inclusions.

Learning Mappings in Mesh-based Simulations A finite element-convolutional neural network model (fe- cnn) for stress field analysis around arbitrary inclusions

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.670197Z

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Observation adbb4a0b-e68b-407f-ab1c-ce48239cea89 · outbound

This paper cites Geometric compression for interactive transmission.

Learning Mappings in Mesh-based Simulations Geometric compression for interactive transmission

Reference 12

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.657157Z

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Observation 50fb26a9-5ea5-47c0-b887-4d005ec3d786 · outbound

This paper cites Learning collision situation to convolutional neural network using collision grid map based on probability scheme.

Learning Mappings in Mesh-based Simulations Learning collision situation to convolutional neural network using collision grid map based on probability scheme

Reference 13

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.644020Z

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Observation e0385376-98a6-4420-99b4-391372bacc0a · outbound

This paper cites Subdle: Identification of substructures in cosmological simulations with deep learning-an image segmentation approach to substructure finding.

Learning Mappings in Mesh-based Simulations Subdle: Identification of substructures in cosmological simulations with deep learning-an image segmentation approach to substructure finding

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.631023Z

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Observation 8fa91fc0-cc5b-4b42-bd84-b1000391d652 · outbound

This paper cites Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction.

Learning Mappings in Mesh-based Simulations Factorized Implicit Global Convolution for Automotive Computational Fluid Dynamics Prediction

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.788244Z

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Observation 9ec1ad84-60b6-4cdb-8089-23d6e4c6c31b · outbound

This paper cites Dgpolarnet: dynamic graph convolution network for lidar point cloud semantic segmentation on polar bev.

Learning Mappings in Mesh-based Simulations Dgpolarnet: dynamic graph convolution network for lidar point cloud semantic segmentation on polar bev

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.618219Z

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Observation 6775a086-b947-49fb-b2f5-568f3b9a558e · outbound

This paper cites Global field reconstruction from sparse sensors with voronoi tessellation-assisted deep learning.

Learning Mappings in Mesh-based Simulations Global field reconstruction from sparse sensors with voronoi tessellation-assisted deep learning

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.604628Z

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Observation 3a70f9c6-b383-4ee3-a22d-4b6b5a68f7a9 · outbound

This paper cites Efficient deep data assimilation with sparse observations and time-varying sensors.

Learning Mappings in Mesh-based Simulations Efficient deep data assimilation with sparse observations and time-varying sensors

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.591755Z

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Observation 801c123b-bd75-4425-a1d7-b92e619599a3 · outbound

This paper cites Applying convolutional neural networks to data on unstructured meshes with space-filling curves.

Learning Mappings in Mesh-based Simulations Applying convolutional neural networks to data on unstructured meshes with space-filling curves

Reference 19

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.577600Z

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Observation 2bfc7bc6-6b1c-4e45-9d14-2931df6dbfff · outbound

This paper cites Phygeonet: Physics-informed geometry-adaptive convo- lutional neural networks for solving parameterized steady-state pdes on irregular domain.

Learning Mappings in Mesh-based Simulations Phygeonet: Physics-informed geometry-adaptive convo- lutional neural networks for solving parameterized steady-state pdes on irregular domain

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.562109Z

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Observation ad5dafa8-a5cd-44d8-8281-7c5fc23c3be4 · outbound

This paper cites Mmgp: a mesh morphing gaussian process-based machine learning method for regression of physical problems under nonparametrized geometrical vari- ability.

Learning Mappings in Mesh-based Simulations Mmgp: a mesh morphing gaussian process-based machine learning method for regression of physical problems under nonparametrized geometrical vari- ability

Reference 21

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.548342Z

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Observation 93ab6233-f708-42a4-8b0f-8e0b1cc7fdf3 · outbound

This paper cites Fourier Neural Operator for Parametric Partial Differential Equations.

Learning Mappings in Mesh-based Simulations Fourier Neural Operator for Parametric Partial Differential Equations

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.815577Z

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Observation ade66f8f-9e31-42e1-a0d2-e6c0e4a13ee8 · outbound

This paper cites Fourier neural operator with learned deformations for pdes on general geometries.

Learning Mappings in Mesh-based Simulations Fourier neural operator with learned deformations for pdes on general geometries

Reference 23

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.535097Z

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Observation 84ebb5e6-34be-453f-961d-5edd9397a407 · outbound

This paper cites Beyond regular grids: Fourier-based neural operators on arbitrary domains.

Learning Mappings in Mesh-based Simulations Beyond regular grids: Fourier-based neural operators on arbitrary domains

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.822956Z

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Observation 5ddc8780-5bdc-469f-9221-d800a0144a2c · outbound

This paper cites Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems.

Learning Mappings in Mesh-based Simulations Mitigating Spectral Bias in Neural Operators via High-Frequency Scaling for Physical Systems

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.830161Z

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Observation 6c15d3b9-6663-4b38-9eb5-1c6b39e3be46 · outbound

This paper cites Parametric encoding with attention and convolution mitigate spectral bias of neural partial differential equation solvers.

Learning Mappings in Mesh-based Simulations Parametric encoding with attention and convolution mitigate spectral bias of neural partial differential equation solvers

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.521550Z

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Observation 09d7053c-d849-4ecf-811e-9bdf0b619b69 · outbound

This paper cites Bubbleml: A multiphase multiphysics dataset and benchmarks for machine learning.

Learning Mappings in Mesh-based Simulations Bubbleml: A multiphase multiphysics dataset and benchmarks for machine learning

Reference 27

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.508210Z

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Observation d77c10e5-a598-4766-b9b5-14522af53dea · outbound

This paper cites Inductive representation learning on large graphs.

Learning Mappings in Mesh-based Simulations Inductive representation learning on large graphs

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.842574Z

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Observation 559d15d2-3d17-49d3-b778-6b3250b1e413 · outbound

This paper cites Learning mesh-based simulation with graph networks.

Learning Mappings in Mesh-based Simulations Learning mesh-based simulation with graph networks

Reference 29

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.484879Z

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Observation 9146082e-3193-4609-93b9-5e5364d9e900 · outbound

This paper cites Convolution, aggrega- tion and attention based deep neural networks for accelerating simulations in mechanics.

Learning Mappings in Mesh-based Simulations Convolution, aggrega- tion and attention based deep neural networks for accelerating simulations in mechanics

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.470689Z

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Observation 12e682f0-c1ab-4185-8ce2-aaa87abfa98c · outbound

This paper cites Graph neural networks and implicit neural representation for near- optimal topology prediction over irregular design domains.

Learning Mappings in Mesh-based Simulations Graph neural networks and implicit neural representation for near- optimal topology prediction over irregular design domains

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.456667Z

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Observation 76cfc47f-4506-4090-a201-f0944818f933 · outbound

This paper cites Graph neural network-based surrogate models for finite element analysis.

Learning Mappings in Mesh-based Simulations Graph neural network-based surrogate models for finite element analysis

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.443204Z

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Observation 19f36c17-17f8-4c02-a437-0cf61d78ab2e · outbound

This paper cites A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials.

Learning Mappings in Mesh-based Simulations A graph-based probabilistic geometric deep learning framework with online enforcement of physical constraints to predict the criticality of defects in porous materials

Reference 33

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.429716Z

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Observation 49436b1c-be8c-4d43-bc0f-ac56e9a1221f · outbound

This paper cites Understanding gnn computational graph: A coordinated computation, io, and memory perspective.

Learning Mappings in Mesh-based Simulations Understanding gnn computational graph: A coordinated computation, io, and memory perspective

Reference 34

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.415706Z

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Observation 54a966fc-3f59-4ada-b5fb-bf3b145a96cc · outbound

This paper cites Pointnet: Deep learning on point sets for 3d classification and segmentation.

Learning Mappings in Mesh-based Simulations Pointnet: Deep learning on point sets for 3d classification and segmentation

Reference 35

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.874151Z

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Observation b6fb6b24-623c-40f6-a0b1-95d1ca6f2992 · outbound

This paper cites Pointnet++: Deep hierarchical feature learning on point sets in a metric space.

Learning Mappings in Mesh-based Simulations Pointnet++: Deep hierarchical feature learning on point sets in a metric space

Reference 36

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.878247Z

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Observation 3803b80c-9057-41a3-96b4-30b360765732 · outbound

This paper cites A point-cloud deep learning framework for predic- tion of fluid flow fields on irregular geometries.

Learning Mappings in Mesh-based Simulations A point-cloud deep learning framework for predic- tion of fluid flow fields on irregular geometries

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.384506Z

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Observation cf3f36d6-8771-45b1-bc78-b3e9363f0c31 · outbound

This paper cites Kolmogorov–arnold pointnet: Deep learning for prediction of fluid fields on irregular geometries.

Learning Mappings in Mesh-based Simulations Kolmogorov–arnold pointnet: Deep learning for prediction of fluid fields on irregular geometries

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.371351Z

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Observation f53e8eee-5699-43ef-926f-bbcfd9860d0e · outbound

This paper cites Physics-informed pointnet: On how many irregu- lar geometries can it solve an inverse problem simultaneously? application to linear elasticity.

Learning Mappings in Mesh-based Simulations Physics-informed pointnet: On how many irregu- lar geometries can it solve an inverse problem simultaneously? application to linear elasticity

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.357923Z

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Observation a98adf4a-c7c4-4742-8d15-bca92a7c35cb · outbound

This paper cites Tree species classification using ground-based lidar data by various point cloud deep learning methods.

Learning Mappings in Mesh-based Simulations Tree species classification using ground-based lidar data by various point cloud deep learning methods

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.344460Z

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Observation b1865081-1f2b-449d-ad35-4d4c9ff372cb · outbound

This paper cites Gnot: A general neural operator transformer for operator learning.

Learning Mappings in Mesh-based Simulations Gnot: A general neural operator transformer for operator learning

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.331131Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 789327a9-3f9b-456b-b837-a8f3a870c49b · outbound

This paper cites PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks.

Learning Mappings in Mesh-based Simulations PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks

Reference 42

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.901607Z

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Observation ef1a6721-663f-4444-8b22-3676a5e29a2d · outbound

This paper cites Transformer for Partial Differential Equations' Operator Learning.

Learning Mappings in Mesh-based Simulations Transformer for Partial Differential Equations' Operator Learning

Reference 43

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.905996Z

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Observation add094b8-9f33-4edd-b83e-dcd45b20c57f · outbound

This paper cites Machine learning for modelling unstructured grid data in computational physics: a review.

Learning Mappings in Mesh-based Simulations Machine learning for modelling unstructured grid data in computational physics: a review

Reference 44

Resolution: verified exact; local_arxiv, observed 2026-08-07T00:50:34.036776Z

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Observation d00e7ce1-ed4d-4dd9-84a9-fcc2ecd2bb52 · outbound

This paper cites Attention is all you need.

Learning Mappings in Mesh-based Simulations Attention is all you need

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.317429Z

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No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation e170a1f6-7609-488d-911e-3eb64e15df1c · outbound

This paper cites Text2PDE: Latent Diffusion Models for Accessible Physics Simulation.

Learning Mappings in Mesh-based Simulations Text2PDE: Latent Diffusion Models for Accessible Physics Simulation

Reference 46

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.918664Z

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Observation 9ac1c9f6-53ff-4138-960b-210e70320ff8 · outbound

This paper cites Real-time single image and video super-resolution using an efficient sub- pixel convolutional neural network.

Learning Mappings in Mesh-based Simulations Real-time single image and video super-resolution using an efficient sub- pixel convolutional neural network

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.302459Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation b8f7de67-2928-4af4-8d4a-f7b882060012 · outbound

This paper cites An isotropic 3 × 3 image gradient operater.

Learning Mappings in Mesh-based Simulations An isotropic 3 × 3 image gradient operater

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.288305Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 80a4b645-8880-4a83-8661-2d787340c0a8 · outbound

This paper cites an unresolved cited work.

Learning Mappings in Mesh-based Simulations Unresolved cited work

Reference 49

Resolution: unresolved; raw_fallback, observed 2026-08-07T00:50:34.274836Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 1925faac-74a1-4a56-ae3d-f0348163d8f5 · outbound

This paper cites The well: a large-scale collection of diverse physics simulations for machine learning.

Learning Mappings in Mesh-based Simulations The well: a large-scale collection of diverse physics simulations for machine learning

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.261476Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation eecaec64-476b-46ff-b58b-afddda825d4b · outbound

This paper cites Clawpack: building an open source ecosystem for solving hyperbolic pdes.

Learning Mappings in Mesh-based Simulations Clawpack: building an open source ecosystem for solving hyperbolic pdes

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-07T00:50:34.247323Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-23T06:30:58.430688+00:00.

Observation 07a86c36-0ca2-4f47-b308-9728882749a0 · outbound

This paper cites Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO).

Learning Mappings in Mesh-based Simulations Real-time Inference and Extrapolation via a Diffusion-inspired Temporal Transformer Operator (DiTTO)

Reference 52

Resolution: unresolved; no resolver link, observed 2026-08-07T00:50:33.965237Z

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