A method called the degeneracy distillery uses symbolic transformations to flatten the Fisher information matrix globally from simulations alone, identifying independent parameter combinations and reducing neural posterior estimation simulation budgets by up to 10x.
SIAM Review42(4), 599– 653 (2000)
6 Pith papers cite this work. Polarity classification is still indexing.
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Isabelle/HOL proofs establish conservation, monotonicity, compartment bounds, and threshold conditions for the SIR ODE by bridging AFP local flows to global forward solutions with reusable scalar lemmas.
Two hybrid Bayesian surrogate training approaches integrate simulation and real-world data via a weighting strategy independent of surrogate family, shown in synthetic and real case studies to improve accuracy and diagnose simulation issues.
M-SPICE uses attention-based multimodal fusion to integrate region-level temporal data with spatially localized auxiliary signals for improved epidemic forecasting on COVID-19, influenza, and ILI tasks.
A nine-compartment nonlinear model with two viral strains and PCHIP-based time-varying parameters is calibrated to Italian COVID-19 third-wave data, achieving high R^2 fits and proving analytical stability properties.
Introduces phenomenological model R_eff = β(1-ρ)(1-τ)(1-γρτ) for coordination under AGI decision velocity, with phase transition and proposed randomized trial.
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The Degeneracy Distillery
A method called the degeneracy distillery uses symbolic transformations to flatten the Fisher information matrix globally from simulations alone, identifying independent parameter combinations and reducing neural posterior estimation simulation budgets by up to 10x.
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Beyond Time Series: Spatial Reasoning for Epidemic Forecasting via Multimodal Learning
M-SPICE uses attention-based multimodal fusion to integrate region-level temporal data with spatially localized auxiliary signals for improved epidemic forecasting on COVID-19, influenza, and ILI tasks.