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Found 3 papers, 1 papers with code
Using Gaussian Processes to Design Dynamic Experiments for Black-Box Model Discrimination under Uncertainty
Typically, several rival mechanistic models can explain the available data, so design of dynamic experiments for model discrimination helps optimally collect additional data by finding experimental settings that maximise model prediction divergence.
GPdoemd: a Python package for design of experiments for model discrimination
Given rival mathematical models and an initial experimental data set, optimal design of experiments suggests maximally informative experimental observations that maximise a design criterion weighted by prediction uncertainty.
Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches
Healthcare companies must submit pharmaceutical drugs or medical devices to regulatory bodies before marketing new technology.
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