# Fast Neural Network Predictions from Constrained Aerodynamics Datasets

arXiv: https://arxiv.org/abs/1902.00091 · HTML: https://mlsanity.com/paper/1902.00091/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2019.

## Built on by (most cited first, 7 of 7)

- [RANS-PINN based Simulation Surrogates for Predicting Turbulent Flows](https://mlsanity.com/paper/2306.06034/) (2023)
- [Demystifying the Data Need of ML-surrogates for CFD Simulations](https://mlsanity.com/paper/2205.08355/) (2022)
- [A Manifold-based Airfoil Geometric-feature Extraction and Discrepant Data Fusion Learning Method](https://mlsanity.com/paper/2206.12254/) (2022)
- [A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification](https://mlsanity.com/paper/2203.12236/) (2022)
- [Implicit Neural Representation For Accurate CFD Flow Field Prediction](https://mlsanity.com/paper/2408.06486/) (2024)
- [Data-Driven Computing Methods for Nonlinear Physics Systems with Geometric Constraints](https://mlsanity.com/paper/2406.16956/) (2024)
- [VATO: A Vortex-Force-Aware Transformer Operator for Unsteady Separated Aerofoil Flows](https://mlsanity.com/paper/2609.00507/) (2026)

## Abstract (arXiv)

Incorporating computational fluid dynamics in the design process of jets, spacecraft, or gas turbine engines is often challenged by the required computational resources and simulation time, which depend on the chosen physics-based computational models and grid resolutions. An ongoing problem in the field is how to simulate these systems faster but with sufficient accuracy. While many approaches involve simplified models of the underlying physics, others are model-free and make predictions based only on existing simulation data. We present a novel model-free approach in which we reformulate the simulation problem to effectively increase the size of constrained pre-computed datasets and introduce a novel neural network architecture (called a cluster network) with an inductive bias well-suited to highly nonlinear computational fluid dynamics solutions. Compared to the state-of-the-art in model-based approximations, we show that our approach is nearly as accurate, an order of magnitude faster, and easier to apply. Furthermore, we show that our method outperforms other model-free approaches.