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.
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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.
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Certified Qualitative Analysis of the SIR ODE and Reusable Scalar Lemmas in Isabelle/HOL
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.
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Bayesian Surrogate Training on Multiple Data Sources: A Hybrid Modeling Strategy
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.