Tadpole is a pre-trained autoencoder foundation model for 3D PDEs that learns transferable representations from online-generated data and supports efficient fine-tuning for dynamics prediction and other tasks.
Cius, Unitary description of the Jaynes-Cummings model under fractional-time dynamics, Phys
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Fractional time Schrödinger equations applied to the time-dependent Jaynes-Cummings model introduce non-Markovian memory that damps oscillations, controls entanglement, and preserves non-periodic dynamics under sinusoidal coupling.
Applies first-passage times of boundary functionals in stable random processes to estimate reactor power peaks and catastrophic surge probabilities.
citing papers explorer
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Tadpole: Autoencoders as Foundation Models for 3D PDEs with Online Learning
Tadpole is a pre-trained autoencoder foundation model for 3D PDEs that learns transferable representations from online-generated data and supports efficient fine-tuning for dynamics prediction and other tasks.
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Non-Markovian Light-Matter Dynamics in the Time Fractional Jaynes-Cummings Model with Modulated Coupling
Fractional time Schrödinger equations applied to the time-dependent Jaynes-Cummings model introduce non-Markovian memory that damps oscillations, controls entanglement, and preserves non-periodic dynamics under sinusoidal coupling.
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Stochastic Safety Limits and Scale-Dependent Power Fluctuations in Nuclear Reactors: A Critical Scaling Approach
Applies first-passage times of boundary functionals in stable random processes to estimate reactor power peaks and catastrophic surge probabilities.