A label-conditioned drifting model in VAE latent space matches diffusion accuracy for flow surrogates while running two orders of magnitude faster, with a spatial variant for unseen geometries.
Loft , author H
3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
Conditional neural fields combined with LSTM networks predict aircraft ditching loads accurately across heterogeneous spatial discretizations using fewer parameters than convolutional autoencoders.
Introduces a traceable virtual sea trial framework in the MARUS simulator for automated TC and ZZ manoeuvres with data conditioning to produce SI-ready datasets for USV hydrodynamic derivative identification.
citing papers explorer
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Drifting Models for Surrogate Flow Modeling
A label-conditioned drifting model in VAE latent space matches diffusion accuracy for flow surrogates while running two orders of magnitude faster, with a spatial variant for unseen geometries.
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Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
Conditional neural fields combined with LSTM networks predict aircraft ditching loads accurately across heterogeneous spatial discretizations using fewer parameters than convolutional autoencoders.
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Traceable Virtual Sea Trials in the Marine Robotics Unity Simulator for Manoeuvring Assessment of Unmanned Surface Vehicles
Introduces a traceable virtual sea trial framework in the MARUS simulator for automated TC and ZZ manoeuvres with data conditioning to produce SI-ready datasets for USV hydrodynamic derivative identification.