Conditional neural fields combined with LSTM networks predict aircraft ditching loads accurately across heterogeneous spatial discretizations using fewer parameters than convolutional autoencoders.
Eivazi , author S
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2026 2verdicts
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Reviews linear and nonlinear SciML surrogates for coupled fluid flow and transport, with new PINN modeling of turbidity currents and β-VAE mode extraction from Rayleigh-Bénard convection.
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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.