Multi-Fidelity Machine Learning Applied to Steady Fluid Flows | Paper Citation Record

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This paper cites Fundamentals of Hydro- and Aeromechanics.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Fundamentals of Hydro- and Aeromechanics

Reference 1

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Observation 5687713d-ea9f-4a91-93d7-318efb3c0644 · outbound

This paper cites An Introduction to Theoretical and Computational Aerodynamics.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows An Introduction to Theoretical and Computational Aerodynamics

Reference 2

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Observation ae86e08d-5585-48d3-a74b-bc83bf70cf9f · outbound

This paper cites Geuzaine and J.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Geuzaine and J

Reference 3

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This paper cites Fast multipole boundary element method: theory and applications in engineering.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Fast multipole boundary element method: theory and applications in engineering

Reference 4

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This paper cites Abbott and Albert E.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Abbott and Albert E

Reference 5

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Observation 003f4536-7b1c-4f24-be23-c2b1c2dd1e87 · outbound

This paper cites Fundamentals of Aerodynamics, Fifth Edition.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Fundamentals of Aerodynamics, Fifth Edition

Reference 6

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Observation 986d28bb-df4e-4ccd-8aef-06b3a0a6b646 · outbound

This paper cites TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems

Reference 7

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Observation eb57a835-7c85-451a-872d-85cb9db7a382 · outbound

This paper cites Convolutional Neural Networks for Steady Flow Approximation.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Convolutional Neural Networks for Steady Flow Approximation

Reference 8

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Observation c864e0b7-fe7d-4c09-b459-72adf10a5cb0 · outbound

This paper cites A Review of Variational Multiscale Methods for the Simulation of Turbulent Incompressible Flows.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A Review of Variational Multiscale Methods for the Simulation of Turbulent Incompressible Flows

Reference 9

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This paper cites Sobolev Training for Neural Networks.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Sobolev Training for Neural Networks

Reference 10

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Observation 594033fa-bf27-4c24-be0c-f14c56afcb6e · outbound

This paper cites Spiral: A General Framework For Parameter Sensitivity Analysis.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Spiral: A General Framework For Parameter Sensitivity Analysis

Reference 11

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Observation 73ecd2bd-862e-42e6-90b8-14b141e0eee8 · outbound

This paper cites Graph Element Networks: adaptive, structured computation and memory.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Graph Element Networks: adaptive, structured computation and memory

Reference 12

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Observation ceeb21e1-c033-43fc-b511-bb70de4044ef · outbound

This paper cites Concurrent shape and topology optimization for steady conjugate heat transfer.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Concurrent shape and topology optimization for steady conjugate heat transfer

Reference 13

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Observation 624049d6-8a26-498f-97f4-52fcd76f266c · outbound

This paper cites Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial dif- ferential equations

Reference 14

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This paper cites Optimize TensorFlow & Keras models with L-BFGS from TensorFlow Prob- ability.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Optimize TensorFlow & Keras models with L-BFGS from TensorFlow Prob- ability

Reference 15

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Observation d856af40-210f-4d3c-99e6-372f4e70efba · outbound

This paper cites Combining Differen- tiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Combining Differen- tiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction

Reference 16

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Observation 1a9f554c-48f2-4bbb-8e8e-3a249a58194d · outbound

This paper cites Machine Learning for Fluid Mechanics.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Machine Learning for Fluid Mechanics

Reference 17

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Observation 77b90e64-2ee4-4056-af92-f135c0f24f88 · outbound

This paper cites Enhancement of Low Fidelity Fluid Simulations using Machine Learn- ing.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Enhancement of Low Fidelity Fluid Simulations using Machine Learn- ing

Reference 18

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Observation 5d66942c-21e3-4f1b-be27-7ffd002898e1 · outbound

This paper cites Conservative physics- informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Conservative physics- informed neural networks on discrete domains for conservation laws: Applications to forward and inverse problems

Reference 19

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Observation af7f8e17-75b1-4244-9888-d0c7f6c3e5d9 · outbound

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

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Fourier Neural Operator for Parametric Partial Differential Equations

Reference 20

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Observation b91d5ed5-de7e-4044-89f8-415f5f7ef428 · outbound

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

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Neural Operator: Graph Kernel Network for Partial Differential Equations

Reference 21

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Observation ba3fdf00-4c05-46d5-87ed-45b7c1c8299f · outbound

This paper cites Concurrent shape and topology optimization for unsteady conjugate heat transfer.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Concurrent shape and topology optimization for unsteady conjugate heat transfer

Reference 22

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Observation fb43a7b1-ab5b-4752-a681-3ae8cbc54df5 · outbound

This paper cites Physics-informed neural net- works for high-speed flows.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural net- works for high-speed flows

Reference 23

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Observation 49ea6f72-8120-4934-a183-ff03a229d17c · outbound

This paper cites A turbulent eddy-viscosity surrogate modeling framework for Reynolds- Averaged Navier-Stokes simulations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A turbulent eddy-viscosity surrogate modeling framework for Reynolds- Averaged Navier-Stokes simulations

Reference 24

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Observation 47fd9128-3e4c-485f-bcd9-c83c59c2a400 · outbound

This paper cites CFDNet: A Deep Learning-Based Accelerator for Fluid Simu- lations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows CFDNet: A Deep Learning-Based Accelerator for Fluid Simu- lations

Reference 25

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This paper cites Hidden fluid mechanics: Learn- ing velocity and pressure fields from flow visualizations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Hidden fluid mechanics: Learn- ing velocity and pressure fields from flow visualizations

Reference 26

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This paper cites Physics-informed deep learning for incompress- ible laminar flows.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed deep learning for incompress- ible laminar flows

Reference 27

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Observation 502687e5-6986-425e-a8b2-20fc38cf0884 · outbound

This paper cites Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

Reference 28

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This paper cites Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Deep Learning Methods for Reynolds-Averaged Navier-Stokes Simulations of Airfoil Flows

Reference 29

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This paper cites Physics-informed neural networks (PINNs) for fluid mechanics: a review.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural networks (PINNs) for fluid mechanics: a review

Reference 30

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This paper cites Transfer learning for deep neural network-based partial differential equa- tions solving.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Transfer learning for deep neural network-based partial differential equa- tions solving

Reference 31

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This paper cites One-Shot Transfer Learning of Physics-Informed Neural Networks.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows One-Shot Transfer Learning of Physics-Informed Neural Networks

Reference 32

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This paper cites PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular do- main.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows PhyGeoNet: Physics-informed geometry-adaptive convolutional neural networks for solving parameterized steady-state PDEs on irregular do- main

Reference 33

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This paper cites NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

Reference 34

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This paper cites Physics-informed machine learning.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed machine learning

Reference 35

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

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries

Reference 36

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This paper cites Characterizing possible failure modes in physics-informed neural networks.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Characterizing possible failure modes in physics-informed neural networks

Reference 37

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Observation 97ab5c4b-6ba3-4fdd-a204-ad9262df3f99 · outbound

This paper cites Learning nonlinear operators via DeepONet based on the universal approxima- tion theorem of operators.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Learning nonlinear operators via DeepONet based on the universal approxima- tion theorem of operators

Reference 38

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This paper cites SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets

Reference 39

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This paper cites A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A Metalearning Approach for Physics-Informed Neural Networks (PINNs): Application to Parameterized PDEs

Reference 40

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This paper cites A Comprehensive Survey on Graph Neural Networks.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A Comprehensive Survey on Graph Neural Networks

Reference 41

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Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics and equality constrained artificial neural net- works: Application to forward and inverse problems with multi-fidelity data fusion

Reference 42

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This paper cites CAN-PINN: A fast physics-informed neural network based on coupled- automatic-numerical differentiation method.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows CAN-PINN: A fast physics-informed neural network based on coupled- automatic-numerical differentiation method

Reference 43

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This paper cites Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration

Reference 44

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This paper cites Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's next

Reference 45

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This paper cites Physics-informed deep-learning applications to experimental fluid mechanics.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed deep-learning applications to experimental fluid mechanics

Reference 46

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This paper cites Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural networks for solving Reynolds-averaged Navier-Stokes equations

Reference 47

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This paper cites Physics-informed neural networks for inverse problems in supersonic flows.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Physics-informed neural networks for inverse problems in supersonic flows

Reference 48

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This paper cites Machine Learning in Aerodynamic Shape Optimization.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Machine Learning in Aerodynamic Shape Optimization

Reference 49

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This paper cites Molnar et al.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Molnar et al

Reference 50

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Observation cee4419c-3297-488c-91b7-ba2cbcee0516 · outbound

This paper cites Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Mosaic flows: A transferable deep learning framework for solving PDEs on unseen domains

Reference 51

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Observation 4a9b4062-f831-4bde-b14e-5cf79d1ac98b · outbound

This paper cites A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows A comprehensive study of non-adaptive and residual-based adaptive sampling for physics-informed neural networks

Reference 52

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This paper cites Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

Reference 53

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Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Unresolved cited work

Reference 54

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Observation 6bb45c3a-939a-4142-a3ca-52c0c26c3044 · outbound

This paper cites Accuracy Improvement Technique of DNN for Accelerating CFD Simu- lator.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Accuracy Improvement Technique of DNN for Accelerating CFD Simu- lator

Reference 55

Resolution: verified exact; doi, observed 2026-08-10T14:59:45.072473Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 96fca9a3-57a1-470f-af3b-0ee71f6937ac · outbound

This paper cites url: https://how5.cenaero.be/ content/vl1-laminar-joukowski-airfoil-re1000.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows url: https://how5.cenaero.be/ content/vl1-laminar-joukowski-airfoil-re1000

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-10T14:59:46.560613Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation f4a6ddbf-1d6e-4c63-b998-93833fc80462 · outbound

This paper cites Fast Neural Network Predictions from Constrained Aerodynamics Datasets.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows Fast Neural Network Predictions from Constrained Aerodynamics Datasets

Reference 57

Resolution: malformed identifier; no resolver link, observed 2026-08-10T14:59:45.023603Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 230a7d86-7535-4927-9024-a5cfd0779a3e · outbound

This paper cites doi: 10.2514/6.2017-1306.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows doi: 10.2514/6.2017-1306

Reference 2017

Resolution: verified exact; doi, observed 2026-08-10T14:59:45.421193Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Observation 2615a603-0a51-48f2-bac3-4d0b40725b85 · outbound

This paper cites url: http://arxiv.org/abs/2208.04280.

Multi-Fidelity Machine Learning Applied to Steady Fluid Flows url: http://arxiv.org/abs/2208.04280

Reference 2022

Resolution: verified exact; doi, observed 2026-08-10T14:59:45.265379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

Coverage vector

Reference resolution

Outbound references

Pith citing papers