Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
Frontiers in Digital Health6 (2024), 1427233
2 Pith papers cite this work. Polarity classification is still indexing.
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Uses MPRK solvers and WENO post-processing to optimize time-varying hyperparameters in existing COVID-19 models and reports 5-day forecasts within 10% error for a Ghana case study.
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Performance and Explainability Requirements of Evolutionary Algorithms in Real-World Physics-Informed Optimization
Domain experts require fast convergence and some explainability from evolutionary algorithms in physics-informed optimization, with other needs varying by problem, revealing an application gap.
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Using Machine Learning to Enhance Hyperparameter Optimization in Pandemic Modeling: Case study of COVID-19 Dynamics in Ghana
Uses MPRK solvers and WENO post-processing to optimize time-varying hyperparameters in existing COVID-19 models and reports 5-day forecasts within 10% error for a Ghana case study.