Hybrid Neural Interpolation of a Sequence of Wind Flows

TL;DR

This paper introduces a hybrid neural interpolation method combining tensor decomposition and neural networks to rapidly and accurately predict urban wind flows, enabling real-time simulations for emergency scenarios.

Contribution

The novel hybrid approach integrates Tucker tensor decomposition with neural networks to interpolate RANS solutions efficiently, reducing training time and improving accuracy over pure neural network models.

Findings

Achieved $R^2 > 0.99$ accuracy in wind flow prediction.

Reduced training time compared to pure neural network models.

Provided a computationally efficient surrogate for real-time urban wind simulation.

Abstract

Rapid and accurate urban wind field prediction is essential for modeling particle transport in emergency scenarios. Traditional Computational Fluid Dynamics (CFD) approaches are too slow for real-time applications, necessitating surrogate models. We develop a hybrid neural interpolation method for constructing surrogate models that can update urban wind maps on timescales aligned with meteorological variations. Our approach combines Tucker tensor decomposition with neural networks to interpolate Reynolds-Averaged Navier-Stokes (RANS) solutions across varying inlet wind angles. The method decomposes high-dimensional velocity, pressure, and eddy viscosity field datasets into a core tensor and factor matrices, then uses Fourier interpolation for angular modes and k-nearest neighbors convolution for spatial interpolation. A neural network correction mitigates interpolation artifacts while preserving physical consistency. We validate the approach on a simple cylinder-sphere configuration and, relative to a strong pure neural network benchmark, achieve comparable or improved accuracy ($R^2 > 0.99$) with significantly reduced training time. The pure NN remains a feasible reference model; the hybrid provides an accelerated approximate alternative that suppresses spurious oscillations, maintains wake dynamics, and demonstrates computational efficiency suitable for real-time urban wind simulation.