Neural Network-Based Parametric Model Reduction for Predicting Turbulent Flow for Different Vehicle Geometries
Numerical simulations in industrial applications often require performing numerous high-precision computations parameterized by specific experimental conditions. For instance, in vehicle body design, aerodynamic simulations are essential for evaluating the aerodynamic characteristics of various proposed body geometries. However, computational resource constraints often become a bottleneck. Therefore, achieving the desired accuracy while minimizing computational cost is crucial. To address this challenge, model reduction methods have been developed to decrease the degrees of freedom by constraining the possible states of a physical system to a lower-dimensional subspace. In particular, red...
| Authors | Kazuto Ando, Rahul Bale, Akiyoshi Kuroda et al. |
|---|---|
| Primary category | cs.CE |
| Published | 23 June 2026 |
| Last updated at source | 23 June 2026 |
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