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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physics.flu-dyn 2years
2026 2verdicts
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A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.
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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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Long-horizon prediction of three-dimensional wall-bounded turbulence with CTA-Swin-UNet and resolvent analysis
A CTA-Swin-UNet with MTFC correction and resolvent-based SLSE reconstruction achieves stable autoregressive prediction of 3D wall-bounded turbulence up to 300 time steps.