Conditional neural field-based reduced order model for dynamic ditching load prediction
Citation Link: https://doi.org/10.15480/882.19152
Publikationstyp
Journal Article
Date Issued
2026-10-22
Sprache
English
TORE-DOI
Journal
Volume
38
Issue
10
Start Page
1
End Page
11
Citation
Physics of Fluids 28 (10): 1-11 (2026)
Publisher DOI
Scopus ID
Publisher
American Institute of Physics (AIP)
Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dynamics. Recently, the use of coordinate-based approaches like conditional neural fields (CNFs) has emerged. Their independence of the spatial discretization is an advantageous feature and a key motivation for computational fluid dynamic applications. This paper discusses the spatiotemporal prediction of aircraft ditching loads using a CNF approach. The model employs a Fourier feature mapping of input coordinates to capture high frequency content. Based on feature-wise linear modulation, the output is conditioned on a latent vector, which is learned by autodecoding. The model is evaluated using two datasets for the dynamic loads on the fuselage of a German Aerospace Center (DLR) D150 aircraft. The first dataset relates to a single fixed spatial discretization, and the second dataset includes data from different discretizations. On the first dataset, the model is compared to a grid-dependent convolutional autoencoder-based model, and both approaches achieve similar reconstruction accuracy, but the CNF requires significantly fewer parameters. When paired with a long short-term memory network in the latent space, both approaches achieve similar spatiotemporal prediction accuracy. Results for the second dataset demonstrate the ability of the CNF-based approach to reconstruct ditching loads accurately for heterogeneous spatial discretizations. In particular, the model can generalize to discretizations not used for training. This allows for flexible use of training datasets generated for different geometries and/or discretizations and the use of the surrogate model to predict loads for different configurations.
DDC Class
629.13: Aviation Engineering
006.32: Neural Networks
Publication version
publishedVersion