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New framework enhances bioprocess digital twin calibration and control

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel framework for calibrating and controlling digital twins of multiscale bioprocesses. This framework utilizes stochastic differential equations and quasi-likelihood estimation, incorporating adjoint sensitivity analysis to manage parameter bias and uncertainty. An Actor-Simulator algorithm is introduced to jointly optimize model parameters, select experiments, and refine control policies, demonstrating improved accuracy and efficiency over existing methods. AI

IMPACT This research could lead to more accurate and efficient digital twins for bioprocesses, potentially impacting drug development and manufacturing.

RANK_REASON The cluster contains a research paper detailing a new framework for bioprocess digital twins. [lever_c_demoted from research: ic=1 ai=0.7]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

New framework enhances bioprocess digital twin calibration and control

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The cluster contains a research paper detailing a new framework for bioprocess digital twins. [lever_c_demoted from research: ic=1 ai=0.7]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Keilung Choy, Wei Xie ·

    Adjoint-Based Calibration and Optimal Control of Stochastic Multiscale Bioprocess Digital Twins

    arXiv:2610.09505v1 Announce Type: cross Abstract: We develop a bias-aware digital-twin calibration and control framework for multiscale bioprocess models within a biological systems-of-systems (Bio-SoS) paradigm. The digital twin is represented by a stochastic differential equati…

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