Bayesian Updating of constitutive parameters under hybrid uncertainties with a novel surrogate model applied to biofilms

TL;DR

This paper introduces a Bayesian updating framework combined with a novel surrogate model to efficiently calibrate biofilm growth models under hybrid uncertainties, validated through case studies with multiple species.

Contribution

It presents a new Bayesian model updating method using a reduced-order surrogate based on TSM for efficient hybrid uncertainty quantification in biofilm modeling.

Findings

Accurately recovers model parameters from sparse, noisy data.

Provides predictions consistent with synthetic data.

Enables single-loop Bayesian inference avoiding nested schemes.

Abstract

Accurate modeling of bacterial biofilm growth is essential for understanding their complex dynamics in biomedical, environmental, and industrial settings. These dynamics are shaped by a variety of environmental influences, including the presence of antibiotics, nutrient availability, and inter-species interactions, all of which affect species-specific growth rates. However, capturing this behavior in computational models is challenging due to the presence of hybrid uncertainties, a combination of epistemic uncertainty (stemming from incomplete knowledge about model parameters) and aleatory uncertainty (reflecting inherent biological variability and stochastic environmental conditions). In this work, we present a Bayesian model updating (BMU) framework to calibrate a recently introduced multi-species biofilm growth model. To enable efficient inference in the presence of hybrid uncertainties, we construct a reduced-order model (ROM) derived using the Time-Separated Stochastic Mechanics (TSM) approach. TSM allows for an efficient propagation of aleatory uncertainty, which enables single-loop Bayesian inference, thereby avoiding the computationally expensive nested (double-loop) schemes typically required in hybrid uncertainty quantification. The BMU framework employs a likelihood function constructed from the mean and variance of stochastic model outputs, enabling robust parameter calibration even under sparse and noisy data. We validate our approach through two case studies: a two-species and a four-species biofilm model. Both demonstrate that our method not only accurately recovers the underlying model parameters but also provides predictive responses consistent with the synthetic data.