# ONERA's CRM WBPN database for machine learning activities, related regression challenge and first results

arXiv: https://arxiv.org/abs/2505.06265 · HTML: https://mlsanity.com/paper/2505.06265/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 33 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 8 of 8)

- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Machine Learning for Fluid Mechanics](https://mlsanity.com/paper/1905.11075/) (2019)
- [Tree-Structured Parzen Estimator: Understanding Its Algorithm Components and Their Roles for Better Empirical Performance](https://mlsanity.com/paper/2304.11127/) (2023)
- [Enhancing Computational Fluid Dynamics with Machine Learning](https://mlsanity.com/paper/2110.02085/) (2021)
- [Codabench: Flexible, Easy-to-Use and Reproducible Benchmarking Platform](https://mlsanity.com/paper/2110.05802/) (2021)
- [Machine Learning in Aerodynamic Shape Optimization](https://mlsanity.com/paper/2202.07141/) (2022)
- [SMT 2.0: A Surrogate Modeling Toolbox with a focus on Hierarchical and Mixed Variables Gaussian Processes](https://mlsanity.com/paper/2305.13998/) (2023)
- [Improving aircraft performance using machine learning: a review](https://mlsanity.com/paper/2210.11481/) (2022)

## Built on by (most cited first, 4 of 4)

- [FluidFlow: a flow-matching generative model for fluid dynamics surrogates on unstructured meshes](https://mlsanity.com/paper/2604.08586/) (2026)
- [BlendedNet++: A dataset and benchmark for field-resolved aerodynamics and inverse design of blended wing body aircraft](https://mlsanity.com/paper/2512.03280/) (2025)
- [Towards a Foundation-Model Paradigm for Aerodynamic Prediction in Three-dimensional Design](https://mlsanity.com/paper/2604.18062/) (2026)
- [Neural Field Ensembles for Aerodynamic Surface Prediction: Winning Solution to the ONERA CRM Wall Distribution 2025 Challenge](https://mlsanity.com/paper/2609.17160/) (2026)

## Abstract (arXiv)

This paper presents a new Computational Fluid Dynamics database, developed at ONERA, to support the advancement of machine learning techniques for aerodynamic field prediction. It contains 468 Reynolds-Averaged Navier-Stokes simulations using the Spalart-Allmaras turbulence model, performed on the NASA/Boeing Common Research Model wing-body-pylon-nacelle configuration. The database spans a wide range of flow conditions, varying Mach number (including transonic regimes), angle of attack (capturing flow separation), and Reynolds number (based on three stagnation pressures, with one setting matching wind tunnel experiments). The quality of the database is assessed, through checking the convergence level of each computation. Based on these data, a regression challenge is defined. It consists in predicting the wall distributions of pressure and friction coefficients for unseen aerodynamic conditions. The 468 simulations are split into training and testing sets, with the training data made available publicly on the Codabench platform. The paper further evaluates several classical machine learning regressors on this task. Tested pointwise methods include Multi-Layer Perceptrons, λ-DNNs, and Decision Trees, while global methods include Multi-Layer Perceptron, k-Nearest Neighbors, Proper Orthogonal Decomposition and IsoMap. Initial performance results, using R2 scores and worst relative mean absolute error metrics, are presented, offering insights into the capabilities of these techniques for the challenge and references for future work.