Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction

• URL: http://arxiv.org/abs/2605.21499v1
• Date: Tue, 05 May 2026 16:03:58 GMT
• Title: Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
• Abstract summary: conditional neural field approach used to predict aircraft ditching loads.<n>Model is evaluated using two datasets for the dynamic loads of the fuselage of a DLR-D150 aircraft.<n>Results show the ability of the neural field-based approach to reconstruct ditching loads accurately for heterogeneous spatial discretizations.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Grid-based neural networks such as convolutional autoencoders are widely used in dimension reduction-based surrogate models for computational fluid dynamics. In recent years, the use of coordinate-based approaches like conditional neural fields has emerged. Their independence of the spatial discretization is a beneficial feature for various applications in computational fluid dynamics. This paper discusses the spatio-temporal prediction of aircraft ditching loads using a conditional neural field approach. The model is evaluated using two datasets for the dynamic loads of the fuselage of a DLR-D150 aircraft, one of which relates to a single fixed spatial discretization and the other that includes data from different discretizations. When paired with a long short-term memory (LSTM) network in the latent space, the neural field-based model achieves a spatio-temporal prediction accuracy for the first data set that is close to that of grid-dependent convolutional autoencoder-based models, and with significantly less parameters. Results for the second data set demonstrate the ability of the neural field-based approach to reconstruct ditching loads accurately for heterogeneous spatial discretizations. This allows for flexible use of training datasets generated for different geometries and/or discretizations, as well as the use of the surrogate model to predict loads for different configurations.

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