Uncertainty Quantification in AI Bioprocess Models

Bayesian Inference Frameworks for Nonlinear Bioprocess Dynamics

This research investigates advanced Bayesian computational methods for quantifying posterior distributions in high-dimensional bioprocess parameter spaces with nonlinear kinetics. The work produces novel probabilistic frameworks that enable rigorous uncertainty propagation through complex fermentation and cell culture models.

Ensemble Methods for Probabilistic Bioprocess Model Validation

This research explores ensemble-based approaches to quantify model discrepancy and prediction intervals in bioprocess simulations across multiple experimental replicates and scales. The work develops metrics for assessing model structural uncertainty and robustness in pharmaceutical manufacturing contexts.

Variational Inference Acceleration for Real-Time Bioprocess Monitoring

This research develops scalable variational inference algorithms to enable fast approximate posterior computation for real-time uncertainty quantification in online bioprocess control. The scientific contribution includes computationally efficient methods for dynamic state estimation with quantified credible intervals.

Global Sensitivity Analysis for Identifiable Bioprocess Parameters

This research applies variance-based global sensitivity analysis to identify which bioprocess parameters are structurally identifiable from experimental data and how parameter uncertainty propagates to model predictions. The work produces systematic frameworks for prioritizing measurement investments and reducing epistemic uncertainty.

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.

Sequential Monte Carlo for Non-stationary Bioprocess Parameter Inference

This research investigates sequential Monte Carlo and particle filtering methods to track time-varying bioprocess parameters under non-stationary conditions and quantify adaptive uncertainty estimates. The work advances Bayesian filtering approaches for capturing parameter drift in long-duration fermentation processes.

Markov Chain Monte Carlo Diagnostics for Bioprocess Model Calibration

This research develops rigorous MCMC diagnostic methodologies to assess convergence and mixing of Bayesian samplers applied to bioprocess model calibration with hundreds of experimental observations. The scientific contribution includes advanced convergence testing frameworks that ensure reliable uncertainty quantification in posterior distributions.

Information-Theoretic Optimal Experimental Design for Bioprocess Characterization

This research applies expected information gain and entropy reduction principles to design optimal bioprocess experiments that minimize parameter uncertainty and model discrepancy. The work produces sequential design algorithms that guide adaptive experimentation strategies for maximal knowledge acquisition.

Multi-fidelity Uncertainty Quantification for Scale-up Bioprocess Predictions

This research develops multi-fidelity Bayesian methods that leverage data from lab-scale, pilot-scale, and manufacturing-scale bioprocesses to quantify systematic uncertainties in process scale-up. The contribution includes hierarchical frameworks for integrating diverse data sources to improve prediction confidence at commercial scales.

Sparse Polynomial Chaos Expansion for Parametric Bioprocess Uncertainty

This research applies compressed sensing and sparse grid methods to construct efficient polynomial chaos surrogate models for quantifying output uncertainty from high-dimensional bioprocess parameter spaces. The scientific contribution includes methods for global uncertainty quantification with reduced computational cost for complex mechanistic models.