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Adjoint-based calibration and optimal control of stochastic bioprocess twins
A calibration and control method is developed for a multiscale bioprocess digital twin using sparse observations, quasi-likelihood estimation and adjoint sensitivity analysis. Parameter uncertainty then feeds into policy optimization and adaptive experiment design.
Why it matters
Accounting for bias and calibration error propagation into the objective shows how to build models fit for decisions, not just description.
Relevant to DT products
- Original headline
- Adjoint-Based Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins
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