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Acceleration of Rans Solver Convergence Via Initialization with Wake Extension Models

https://doi.org/10.2139/ssrn.4750758
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Prediction of aerodynamic flow fields using convolutional neural networks
inferredcites →https://doi.org/10.1007/s10409-021-01148-1openalex-extracted
Physics-informed neural networks (PINNs) for fluid mechanics: a review
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State-of-the-art in aerodynamic shape optimisation methods
inferredcites →https://doi.org/10.1016/j.compfluid.2022.105707openalex-extracted
Towards high-accuracy deep learning inference of compressible flows over aerofoils
inferredcites →https://doi.org/10.1016/j.jcp.2020.109951openalex-extracted
NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations
inferredcites →https://doi.org/10.1016/j.jcp.2025.113871openalex-extracted
Neural operator-based super-fidelity: A warm-start approach for accelerating steady-state simulations
inferredcites →https://doi.org/10.1017/aer.2015.10openalex-extracted
On the role and challenges of CFD in the aerospace industry
inferredcites →https://doi.org/10.1017/cbo9780511760396openalex-extracted
Verification and Validation in Scientific Computing
inferredcites →https://doi.org/10.1063/5.0033376openalex-extracted
A point-cloud deep learning framework for prediction of fluid flow fields on irregular geometries
inferredcites →https://doi.org/10.1063/5.0095270openalex-extracted
Physics-informed neural networks for solving Reynolds-averaged Navier–Stokes equations
inferredcites →https://doi.org/10.1080/10618562.2022.2154758openalex-extracted
Multi-Fidelity Machine Learning Applied to Steady Fluid Flows
inferredcites →https://doi.org/10.1093/imamat/hxae011openalex-extracted
Can physics-informed neural networks beat the finite element method?
inferredcites →https://doi.org/10.1109/pact52795.2021.00031openalex-extracted
SURFNet: Super-Resolution of Turbulent Flows with Transfer Learning using Small Datasets
inferredcites →https://doi.org/10.1115/1.4048221openalex-extracted
Generalizability of Convolutional Encoder–Decoder Networks for Aerodynamic Flow-Field Prediction Across Geometric and Physical-Fluidic Variations
inferredcites →https://doi.org/10.1145/2939672.2939738openalex-extracted
Convolutional Neural Networks for Steady Flow Approximation
inferredcites →https://doi.org/10.1145/3392717.3392772openalex-extracted
CFDNet
inferredcites →https://doi.org/10.21105/joss.00747openalex-extracted
NN-SVG: Publication-Ready Neural Network Architecture Schematics
inferredcites →https://doi.org/10.25080/majora-212e5952-005openalex-extracted
Experience report of physics-informed neural networks in fluid simulations: pitfalls and frustration
inferredcites →https://doi.org/10.2514/1.c037184openalex-extracted
Fourth AIAA High-Lift Prediction Workshop: Fixed-Grid Reynolds-Averaged Navier–Stokes Summary
inferredcites →https://doi.org/10.2514/1.j058291openalex-extracted
Deep Learning Methods for Reynolds-Averaged Navier–Stokes Simulations of Airfoil Flows
inferredcites →https://doi.org/10.2514/6.2003-411openalex-extracted
Best Practices for Reduction of Uncertainty in CFD Results (Invited)
inferredcites →https://doi.org/10.2514/6.2022-1437openalex-extracted
Accuracy Improvement Technique of DNN for Accelerating CFD Simulator
inferredcites →https://doi.org/10.2514/6.2023-1089openalex-extracted
Evolutionary Multiobjective Aerodynamic Design Optimization Using CFD Incorporating Deep Neural Network
inferredcites →https://doi.org/10.48550/arxiv.1608.03983openalex-extracted
SGDR: Stochastic Gradient Descent with Warm Restarts
inferredcites →https://doi.org/10.48550/arxiv.2007.04439openalex-extracted
Combining Differentiable PDE Solvers and Graph Neural Networks for Fluid Flow Prediction
inferredcites →https://doi.org/10.48550/arxiv.2312.04289openalex-extracted
Fast simulation of airfoil flow field via deep neural network
inferredcites →https://doi.org/10.48550/arxiv.2005.04485openalex-snapshot
CFDNet: a deep learning-based accelerator for fluid simulations
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