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Gaussian Process Emulation with Quantified Model Discrepancy

Ai Bioprocess Optimization

Uncertainty Quantification in AI Bioprocess Models

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Ai Bioprocess Optimization › Uncertainty Quantification in AI Bioprocess Models

Gaussian Process Emulation with Quantified Model Discrepancy

This research develops surrogate modeling techniques using Gaussian processes that explicitly account for structural model discrepancy between mechanistic bioprocess models and experimental reality. The contribution includes principled uncertainty quantification methods that separate aleatory and epistemic sources of model inadequacy.

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📚 Academic: Thesis & PPT assistance included🧪 Tech: Master the protocols hands-on📝 Research > 3 months: Publication co-authorship in a Scopus-indexed journal

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