PIDO trains an auto-decoded latent-space dynamics model with physics-informed losses and two regularizers, reporting lower errors on unseen initial conditions, coefficients, and time horizons than PI-DeepONet, PINODE, and MAD.
Uncertainty quantification in estimating blood alcohol concentration from transdermal alcohol level with physics-informed neural networks,
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Advancing Generalization in PINNs through Latent-Space Representations
PIDO trains an auto-decoded latent-space dynamics model with physics-informed losses and two regularizers, reporting lower errors on unseen initial conditions, coefficients, and time horizons than PI-DeepONet, PINODE, and MAD.