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Paper Citation Record · LEDGER

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems

As of 10 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:2602.11208.

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2602.11208 v2

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measured 35 of 35 reference resolution

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measured 35 of 35 standing notices

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35 of 35 outbound references displayed

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Outbound references

Observation ea2dd7ca-0b85-4fc1-b9cd-b650b0b79350 · outbound

This paper cites 13 Adaptive Physics Transformer for Subsurface Energy Systems B.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems 13 Adaptive Physics Transformer for Subsurface Energy Systems B

Reference 2

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Observation d4ecd2ea-7832-4dd7-b185-13697478ead4 · outbound

This paper cites doi: 10.1016/j.neunet.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems doi: 10.1016/j.neunet

Reference 7

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This paper cites an unresolved cited work.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Unresolved cited work

Reference 8

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Observation 20af79d0-36a3-4f23-941a-2cacfdcbbda2 · outbound

This paper cites Nonlocality and Nonlinearity Implies Universality in Operator Learning.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Nonlocality and Nonlinearity Implies Universality in Operator Learning

Reference 9

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Observation ed0e9818-f64b-4c08-9899-957bd73712a5 · outbound

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

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Fourier Neural Operator for Parametric Partial Differential Equations

Reference 10

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Observation 62216822-a2e6-4048-be4c-385c58fe756d · outbound

This paper cites Each row compares predicted pressure fields (top) and absolute error maps (bottom) against the CFD ground truth.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Each row compares predicted pressure fields (top) and absolute error maps (bottom) against the CFD ground truth

Reference 11

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Observation bee48cdf-ba5e-4f97-8ef9-be217e8abbe1 · outbound

This paper cites DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems DeepONet: Learning nonlinear operators for identifying differential equations based on the universal approximation theorem of operators

Reference 12

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Observation 6e92793a-4da3-42a3-9f52-6bdeaf604a7b · outbound

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

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Learning Mesh-Based Simulation with Graph Networks

Reference 13

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Observation 5401b20a-4518-4852-87e1-13ab66c64c35 · outbound

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

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems U-net: Con- volutional networks for biomedical image segmentation

Reference 14

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Observation 8dd392b1-5d28-4cc9-a3ce-10c8b0cb72ae · outbound

This paper cites LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems LaB-GATr: geometric algebra transformers for large biomedical surface and volume meshes

Reference 15

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Observation 89964011-9ea8-4d1b-878a-4545cd20d842 · outbound

This paper cites Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Unisoma: A Unified Transformer-based Solver for Multi-Solid Systems

Reference 16

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Observation b587ebab-ad7b-45c9-9c12-7b361feae58b · outbound

This paper cites N., Kaiser,Ł., and Polosukhin, I.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems N., Kaiser,Ł., and Polosukhin, I

Reference 17

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Observation 0dd611e8-628a-4830-afb5-fdaf64411a52 · outbound

This paper cites CViT: Continuous Vision Transformer for Operator Learning.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems CViT: Continuous Vision Transformer for Operator Learning

Reference 18

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Observation 5309d45c-968e-438e-bd4e-63a8a999c32e · outbound

This paper cites Transolver: A Fast Transformer Solver for PDEs on General Geometries.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Transolver: A Fast Transformer Solver for PDEs on General Geometries

Reference 19

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Observation 5ca3291b-4c5d-4033-a886-f96b37fd41f8 · outbound

This paper cites Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Transolver++: An Accurate Neural Solver for PDEs on Million-Scale Geometries

Reference 20

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Observation 50d853d5-8985-422b-ac88-a264e5442467 · outbound

This paper cites FNO and U-FNO results are from Badawi & Gildin (2025).

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems FNO and U-FNO results are from Badawi & Gildin (2025)

Reference 25

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Unresolved cited work

Reference 27

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This paper cites Since the PEBI mesh is unstructured with varying topology across cases, the simulation data must be interpolated onto a regular grid before training.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Since the PEBI mesh is unstructured with varying topology across cases, the simulation data must be interpolated onto a regular grid before training

Reference 28

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This paper cites All models are evaluated with batch size 1 on an NVIDIA A100-SXM4-80GB GPU under identical inference settings.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems All models are evaluated with batch size 1 on an NVIDIA A100-SXM4-80GB GPU under identical inference settings

Reference 29

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Unresolved cited work

Reference 30

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This paper cites Each row compares predicted pressure fields (top) and absolute error maps (bottom) against the CFD ground truth.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Each row compares predicted pressure fields (top) and absolute error maps (bottom) against the CFD ground truth

Reference 34

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This paper cites A vertical injection well with a radius of 0.1 m delivers wastewater at a constant rate into a radially symmetric system x(r, z).

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems A vertical injection well with a radius of 0.1 m delivers wastewater at a constant rate into a radially symmetric system x(r, z)

Reference 35

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This paper cites We found that using only 1024 supernodes for this dataset suffices to achieve good performance.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems We found that using only 1024 supernodes for this dataset suffices to achieve good performance

Reference 192

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Observation 744be830-ce65-4e1c-891e-4d7c403015b7 · outbound

This paper cites Model Reduction and Neural Networks for Parametric PDEs.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Model Reduction and Neural Networks for Parametric PDEs

Reference 2010

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This paper cites DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems DrivAerNet: A Parametric Car Dataset for Data-Driven Aerodynamic Design and Prediction

Reference 2014

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This paper cites Intuitively, CNNs were used for regular Cartesian discretizations (Zhu & Zabaras, 2018; Mo et al., 2019; Wen et al., 2022; Tang et al., 2022; Wen et al., 2023a; 2021).

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Intuitively, CNNs were used for regular Cartesian discretizations (Zhu & Zabaras, 2018; Mo et al., 2019; Wen et al., 2022; Tang et al., 2022; Wen et al., 2023a; 2021)

Reference 2015

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Since PEBI meshes are orthogonal by construction, the flow simulations can be performed with a TPFA-based finite-volume scheme without compromising solution accuracy

Reference 2016

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Observation f8ab069c-aae5-400b-9f52-c8be1593bd8d · outbound

This paper cites Predicting Physics in Mesh-reduced Space with Temporal Attention.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Predicting Physics in Mesh-reduced Space with Temporal Attention

Reference 2017

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Related Work A.1

Reference 2018

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Message Passing Neural PDE Solvers

Reference 2020

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Unresolved cited work

Reference 2021

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Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems B., Levine, M

Reference 2022

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This paper cites Bartolucci, F., Raonic, B., Molinaro, R., de B ´ezenac, E., Mishra, S., and Alaifari, R.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Bartolucci, F., Raonic, B., Molinaro, R., de B ´ezenac, E., Mishra, S., and Alaifari, R

Reference 2023

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This paper cites Ab-upt: Scaling neural cfd surrogates for high-fidelity automotive aero- dynamics simulations via anchored-branched universal physics transformers.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Ab-upt: Scaling neural cfd surrogates for high-fidelity automotive aero- dynamics simulations via anchored-branched universal physics transformers

Reference 2024

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This paper cites Neural Operators with Localized Integral and Differential Kernels.

Adaptive Physics Transformer with Fused Global-Local Attention for Subsurface Energy Systems Neural Operators with Localized Integral and Differential Kernels

Reference 2025

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