Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification | Paper Citation Record

Observation 168eb965-bf50-42f7-ae43-e05e7038955d · outbound

This paper cites Direct numerical simulation: a tool in turbulence research.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Direct numerical simulation: a tool in turbulence research

Reference 1

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This paper cites Direct numerical simulation of turbulent channel flow up to reτ= 590.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Direct numerical simulation of turbulent channel flow up to reτ= 590

Reference 2

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This paper cites Direct numerical simulation of turbulent channel flow up to.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Direct numerical simulation of turbulent channel flow up to

Reference 3

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Observation 48efce51-df89-4a7b-b962-e154a9972203 · outbound

This paper cites New trends in large-eddy simulations of turbulence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification New trends in large-eddy simulations of turbulence

Reference 4

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Observation 8cf6580f-8673-4aa2-b7b0-07e13153696a · outbound

This paper cites Large-eddy simulations of turbulence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Large-eddy simulations of turbulence

Reference 5

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Observation f8fbe1b3-342a-4c68-a85e-7d289218fc7e · outbound

This paper cites Analytical methods for the development of reynolds-stress closures in turbulence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Analytical methods for the development of reynolds-stress closures in turbulence

Reference 6

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Observation 020a05f4-eee1-4c9b-b178-03797764a43f · outbound

This paper cites Progress in the development of a reynolds-stress turbulence closure.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Progress in the development of a reynolds-stress turbulence closure

Reference 7

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Observation cfc31063-56bf-4845-a862-f8564ea0bba5 · outbound

This paper cites Toward approximating non-local dynamics in single-point pressure–strain correlation closures.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Toward approximating non-local dynamics in single-point pressure–strain correlation closures

Reference 8

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Observation 7d64a590-770f-4a0c-8884-7783851f8421 · outbound

This paper cites Modelling of rapid pressure—strain in reynolds-stress closures.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Modelling of rapid pressure—strain in reynolds-stress closures

Reference 9

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Observation 40228ebf-56fb-4c40-af99-244923da2c32 · outbound

This paper cites Modelling the pressure–strain correlation of turbulence: an invariant dynamical systems approach.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Modelling the pressure–strain correlation of turbulence: an invariant dynamical systems approach

Reference 10

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Observation 5c8b5364-20dc-435e-ba38-bb38ec316dbb · outbound

This paper cites Development and application of a cubic eddy-viscosity model of turbulence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Development and application of a cubic eddy-viscosity model of turbulence

Reference 11

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Observation 88d8d28d-227a-4af1-b0eb-d3c9a5bb97ad · outbound

This paper cites Linear and nonlinear eddy viscosity models.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Linear and nonlinear eddy viscosity models

Reference 12

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Observation dc6f660b-5275-4b9c-95b9-60585be1554e · outbound

This paper cites A new k- ϵ eddy viscosity model for high reynolds number turbulent flows.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification A new k- ϵ eddy viscosity model for high reynolds number turbulent flows

Reference 13

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Observation b3de0558-e1ce-4528-9254-b1f88ecfacf6 · outbound

This paper cites Eddy viscosity in two and three dimensions.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Eddy viscosity in two and three dimensions

Reference 14

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Observation 3d17fe3b-4bd9-482e-ad79-48a09f7f642f · outbound

This paper cites The numerical computation of turbulent flows.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification The numerical computation of turbulent flows

Reference 15

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Observation 1834b38a-abd6-42b3-9a06-773a82e5483a · outbound

This paper cites Formulation of the kw turbulence model revisited.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Formulation of the kw turbulence model revisited

Reference 16

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.591380Z

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Observation 7303d301-1761-4a2e-b934-c3fd8d04ce62 · outbound

This paper cites Uncertainty quantification: theory, implementation, and applications, volume 12.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Uncertainty quantification: theory, implementation, and applications, volume 12

Reference 17

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Observation bb837427-8b16-44fa-8e97-27678cd0f45e · outbound

This paper cites Turbulence modeling in the age of data.Annual Review of Fluid Mechanics, 51:357–377, 2019.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Turbulence modeling in the age of data.Annual Review of Fluid Mechanics, 51:357–377, 2019

Reference 18

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Observation fdffde5f-4f56-445b-aa9e-2f4ef229ba5c · outbound

This paper cites Status, emerging ideas and future directions of turbulence modeling research in aeronautics.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Status, emerging ideas and future directions of turbulence modeling research in aeronautics

Reference 19

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Observation 65579762-4fb3-4c26-bac1-3988308f8a22 · outbound

This paper cites Bayesian uncertainty quantification applied to rans turbulence models.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Bayesian uncertainty quantification applied to rans turbulence models

Reference 20

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Observation f6a11202-f0a6-48bf-ab8d-c44ebfd364d8 · outbound

This paper cites Sensitivity of flow evolution on turbulence structure.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Sensitivity of flow evolution on turbulence structure

Reference 21

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Observation 2676ccc3-edfd-4395-bf53-77653fc936a6 · outbound

This paper cites Scalable environment for quantification of uncertainty and optimization in industrial applications (sequoia).

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Scalable environment for quantification of uncertainty and optimization in industrial applications (sequoia)

Reference 22

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Observation fe9bd0d9-987d-4867-896a-ba021bc694d6 · outbound

This paper cites Approach for uncertainty of turbulence modeling based on data assimilation technique.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Approach for uncertainty of turbulence modeling based on data assimilation technique

Reference 23

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Observation a90b052b-9b5f-4e9c-8a06-f91c60c424a0 · outbound

This paper cites Quantification of structural uncertainties in the k-w turbulence model.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Quantification of structural uncertainties in the k-w turbulence model

Reference 24

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Observation 24b0109e-65c1-40ad-a5ef-bb1c1ae21700 · outbound

This paper cites Modeling imprecision and uncertainty in preliminary engineer- ing design.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Modeling imprecision and uncertainty in preliminary engineer- ing design

Reference 25

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Observation 97e799d9-e7e3-4fed-a681-b623035b141a · outbound

This paper cites Methodology for managing the effect of uncertainty in simulation-based design.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Methodology for managing the effect of uncertainty in simulation-based design

Reference 26

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.442632Z

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Observation d03d5fa5-4cea-42e9-be92-c9ad117b6d4e · outbound

This paper cites Robust multiattribute decision making under risk and uncer- tainty in engineering design.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Robust multiattribute decision making under risk and uncer- tainty in engineering design

Reference 27

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Observation 68dbb2c4-3e33-4140-96e6-8612005e6afa · outbound

This paper cites Comparative analysis of uncertainty propagation methods for robust engineering design.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Comparative analysis of uncertainty propagation methods for robust engineering design

Reference 28

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Observation bbe4cb35-6505-4120-9409-57f90ce782e6 · outbound

This paper cites Impact of uncertainty quan- tification on design: an engine optimisation case study.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Impact of uncertainty quan- tification on design: an engine optimisation case study

Reference 29

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Observation e23967ac-1cf0-4c79-8739-74c95ac2a2cd · outbound

This paper cites Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Review of uncertainty-based multidisciplinary design optimization methods for aerospace vehicles

Reference 30

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.381774Z

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Observation eeb43a88-c45d-4daf-8c8c-03c2abe7089e · outbound

This paper cites Aerospace applications of optimization under uncer- tainty.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Aerospace applications of optimization under uncer- tainty

Reference 31

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.366829Z

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Observation d42665d1-6859-448c-baba-2fa3ca4f1b85 · outbound

This paper cites Eigenspace perturbations for uncer- tainty estimation of single-point turbulence closures.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Eigenspace perturbations for uncer- tainty estimation of single-point turbulence closures

Reference 32

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.352119Z

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Observation 4318a5b5-a504-47cd-9673-1943ba59759d · outbound

This paper cites Uncertainty estimation module for turbulence model predictions in su2.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Uncertainty estimation module for turbulence model predictions in su2

Reference 33

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Observation 42212779-8c2c-40dc-b3d9-6f5f4b988a73 · outbound

This paper cites RANS predictions for high-speed flows using enveloping models.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification RANS predictions for high-speed flows using enveloping models

Reference 34

Resolution: verified exact; local_arxiv, observed 2026-08-05T10:42:02.814010Z

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Observation 2f2ccf27-d4b1-45f3-bf7d-c06c75565658 · outbound

This paper cites Estimating uncertainty in homogeneous turbulence evolution due to coarse-graining.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Estimating uncertainty in homogeneous turbulence evolution due to coarse-graining

Reference 35

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.322282Z

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Observation 6dd41a02-029b-4998-be68-d7556e21e278 · outbound

This paper cites Uncertainty estimation for reynolds-averaged navier– stokes predictions of high-speed aircraft nozzle jets.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Uncertainty estimation for reynolds-averaged navier– stokes predictions of high-speed aircraft nozzle jets

Reference 36

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.306436Z

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Observation 9559e0db-c0fa-4389-9990-d55a88988131 · outbound

This paper cites Eigenvector perturbation methodology for uncertainty quantification of turbulence models.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Eigenvector perturbation methodology for uncertainty quantification of turbulence models

Reference 37

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.290988Z

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Observation 05b0b2e9-c882-4855-9241-623348b5c929 · outbound

This paper cites Robust shape optimization under model uncertainty of an aircraft wing using proper orthogonal decomposition and inductive design exploration method.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Robust shape optimization under model uncertainty of an aircraft wing using proper orthogonal decomposition and inductive design exploration method

Reference 38

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.276450Z

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Observation e96d4d9b-0172-44a5-a42c-26317e6ac4a1 · outbound

This paper cites Optimization under turbulence model uncertainty for aerospace design.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Optimization under turbulence model uncertainty for aerospace design

Reference 39

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.262733Z

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Observation 5b8bdca4-60be-4009-be93-356819f12707 · outbound

This paper cites Design exploration and optimization under uncertainty.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Design exploration and optimization under uncertainty

Reference 40

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.248342Z

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Observation d4d10665-65d8-4cc5-ae3e-eabfd63b6cdf · outbound

This paper cites Uncertainties quantification in the prediction of the aeroelastic response of the pazy wing tunnel model.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Uncertainties quantification in the prediction of the aeroelastic response of the pazy wing tunnel model

Reference 41

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.234168Z

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Observation 6f06c2c7-7af1-4960-8a4a-94cc5373d886 · outbound

This paper cites Adjoint design optimization under the uncertainty quantification of reynolds-averaged navier-stokes turbulence model.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Adjoint design optimization under the uncertainty quantification of reynolds-averaged navier-stokes turbulence model

Reference 42

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.218726Z

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Observation 4cd7e7c5-c4f6-4a8d-9423-2e621af45d3e · outbound

This paper cites Application of uncertainty quantification of reynolds-averaged navier–stokes models in hypersonic flow.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Application of uncertainty quantification of reynolds-averaged navier–stokes models in hypersonic flow

Reference 43

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.200442Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c9c3134b-729c-496a-bd0b-8bfc2600f905 · outbound

This paper cites Epistemic uncertainty quantification for reynolds-averaged navier-stokes modeling of separated flows over streamlined surfaces.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Epistemic uncertainty quantification for reynolds-averaged navier-stokes modeling of separated flows over streamlined surfaces

Reference 44

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.184918Z

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Observation 8db7201c-0264-43b9-ab7b-9d4ee24020cf · outbound

This paper cites A nonuniform perturbation to quantify rans model uncertainties.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification A nonuniform perturbation to quantify rans model uncertainties

Reference 45

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.169864Z

Source-reported events for the cited work

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Observation a756a011-1a5f-4da3-b792-d118d02d5c93 · outbound

This paper cites Multi- fidelity modeling of probabilistic aerodynamic databases for use in aerospace engineering.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Multi- fidelity modeling of probabilistic aerodynamic databases for use in aerospace engineering

Reference 46

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.154327Z

Source-reported events for the cited work

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Observation a1417dbc-b764-45a0-81f2-ee6098447e8b · outbound

This paper cites A toolset for creation of multi-fidelity probabilistic aerodynamic databases.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification A toolset for creation of multi-fidelity probabilistic aerodynamic databases

Reference 47

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.138683Z

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Observation d4f2d287-341d-4491-a65c-c70a15e56d49 · outbound

This paper cites Model-form uncertainty quantification of reynolds- averaged navier–stokes modeling of flows over a sd7003 airfoil.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Model-form uncertainty quantification of reynolds- averaged navier–stokes modeling of flows over a sd7003 airfoil

Reference 48

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.124361Z

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Observation 898bfb4f-471a-46db-a51e-7ec4c7ae7193 · outbound

This paper cites Machine learning for fluid mechanics.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Machine learning for fluid mechanics

Reference 49

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.109012Z

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Observation bc3ffe77-d05e-48c1-a65a-6e02e81efbf4 · outbound

This paper cites Data-assisted combustion simulations with dynamic submodel assignment using random forests.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Data-assisted combustion simulations with dynamic submodel assignment using random forests

Reference 50

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.092997Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 9e0d530d-2df5-42db-8222-b95a10d02c41 · outbound

This paper cites Interpretable data-driven methods for subgrid-scale closure in les for transcritical lox/gch4 combustion.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Interpretable data-driven methods for subgrid-scale closure in les for transcritical lox/gch4 combustion

Reference 51

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.077399Z

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No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 24559b5b-8c9c-440b-bf20-9006da8d16b1 · outbound

This paper cites Perspectives on machine learning-augmented reynolds-averaged and large eddy simulation models of turbulence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Perspectives on machine learning-augmented reynolds-averaged and large eddy simulation models of turbulence

Reference 52

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.061364Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 416af229-9f98-42af-9829-5b944bbe9d01 · outbound

This paper cites Machine learning methods for data-driven turbulence modeling.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Machine learning methods for data-driven turbulence modeling

Reference 53

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.047150Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 03e1032c-b3af-434f-a080-2c59638dd3b1 · outbound

This paper cites Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Workshop report on basic research needs for scientific machine learning: Core technologies for artificial intelligence

Reference 54

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.032785Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c81b14f8-1a46-4835-8ab7-e165daf0b493 · outbound

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

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Reference 55

Resolution: unresolved; no resolver link, observed 2026-08-05T10:42:02.688284Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 400e891e-6265-46d4-b6f8-9dd8bcb8ad58 · outbound

This paper cites Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Physics constrained unsupervised deep learning for rapid, high resolution scanning coherent diffraction reconstruction

Reference 56

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:03.004932Z

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Observation 2de474d0-424d-4ee2-be8e-832e5f78ecb2 · outbound

This paper cites Turbulent flows, 2001.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Turbulent flows, 2001

Reference 57

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.990759Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4580e363-54b2-48eb-92db-f05e25aa62df · outbound

This paper cites Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Presentation of anisotropy properties of turbulence, invariants versus eigenvalue approaches

Reference 58

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.977379Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 855eadd1-fff7-48b4-a9d3-c8ab30ad5a71 · outbound

This paper cites Turbulent and Non-Turbulent Interfaces in Low Mach Number Airfoil Flows.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Turbulent and Non-Turbulent Interfaces in Low Mach Number Airfoil Flows

Reference 59

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.964297Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation a47573e9-3e3c-49a9-b70b-080532d7ab23 · outbound

This paper cites A correlation-based transition model using local variables for unstructured parallelized cfd codes.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification A correlation-based transition model using local variables for unstructured parallelized cfd codes

Reference 60

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.950130Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 74f63270-6130-4491-b00d-4aeb1541c51f · outbound

This paper cites Correlation-based transition modeling for unstructured paral- lelized computational fluid dynamics codes.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Correlation-based transition modeling for unstructured paral- lelized computational fluid dynamics codes

Reference 61

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.935981Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 86fd7351-638f-4066-9e5c-e39e5a6b4632 · outbound

This paper cites Quantification of reynolds-averaged-navier–stokes model-form uncertainty in transitional boundary layer and airfoil flows.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Quantification of reynolds-averaged-navier–stokes model-form uncertainty in transitional boundary layer and airfoil flows

Reference 62

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.920164Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 4769af9f-6f33-4b4a-9a1c-2b4ea496a4c8 · outbound

This paper cites A hybrid approach combining dns and rans simulations to quantify uncertainties in turbulence modelling.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification A hybrid approach combining dns and rans simulations to quantify uncertainties in turbulence modelling

Reference 63

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.905866Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation c6cc7a8b-43d6-4de2-989a-a154508275cb · outbound

This paper cites Implicit large eddy simulation of low-reynolds-number transi- tional flow past the sd7003 airfoil.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Implicit large eddy simulation of low-reynolds-number transi- tional flow past the sd7003 airfoil

Reference 64

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.890332Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7451b0cb-925a-4e93-a24b-ecfa638e10f3 · outbound

This paper cites an unresolved cited work.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Unresolved cited work

Reference 65

Resolution: unresolved; raw_fallback, observed 2026-08-05T10:42:02.874079Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation dffddcea-4cef-476d-9f53-68593342bcc0 · outbound

This paper cites Data driven physics constrained perturbations for turbulence model uncertainty estimation.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Data driven physics constrained perturbations for turbulence model uncertainty estimation

Reference 66

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.859264Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 5412ac2e-bef0-444b-b800-423a69b53eb0 · outbound

This paper cites Estimating rans model uncertainty using machine learning.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Estimating rans model uncertainty using machine learning

Reference 67

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.844178Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

Observation 7364fc0f-248a-4ca3-a42b-d01534325bfb · outbound

This paper cites Evaluation of physics constrained data- driven methods for turbulence model uncertainty quantification.

Towards Physics Constrained Deep Learning Based Turbulence Model Uncertainty Quantification Evaluation of physics constrained data- driven methods for turbulence model uncertainty quantification

Reference 68

Resolution: verified fuzzy; raw_fallback, observed 2026-08-05T10:42:02.829080Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-10T06:31:04.303077+00:00.

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