PILL-CoDe co-optimizes polypill geometry via supershapes and excipient maps via neural networks to match target drug-release curves using end-to-end differentiable modified Allen-Cahn and Fickian diffusion models.
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3 Pith papers cite this work. Polarity classification is still indexing.
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Pith papers citing it
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LaSDI-IT learns latent linear dynamics for interface tracking via a revised autoencoder and Gaussian process interpolation, achieving under 9% error and 106x speedup on shock-induced pore collapse in high explosives.
citing papers explorer
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PILL-CoDe: Inverse Design of Polypills via Automatic Differentiation for Prescribed Drug-Release Kinetics
PILL-CoDe co-optimizes polypill geometry via supershapes and excipient maps via neural networks to match target drug-release curves using end-to-end differentiable modified Allen-Cahn and Fickian diffusion models.
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Latent Space Dynamics Identification for Interface Tracking with Application to Shock-Induced Pore Collapse
LaSDI-IT learns latent linear dynamics for interface tracking via a revised autoencoder and Gaussian process interpolation, achieving under 9% error and 106x speedup on shock-induced pore collapse in high explosives.
- A Deep Ritz Method for High-Dimensional Steady States of the Cahn-Hilliard Equation