Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling

TL;DR

This paper introduces new flexible methods for parameter estimation in mechanistic models using data-consistent inversion, addressing biases in Bayesian approaches and leveraging GANs for improved inference.

Contribution

It proposes novel stochastic inverse problem techniques, including GAN-based methods and constrained optimization reformulations, for more accurate parameter estimation.

Findings

New GAN-based methods for data-consistent inversion.

Reformulation of SIP using constrained optimization.

Demonstrated effectiveness of methods on physical system models.

Abstract

Predictions for physical systems often rely upon knowledge acquired from ensembles of entities, e.g., ensembles of cells in biological sciences. For qualitative and quantitative analysis, these ensembles are simulated with parametric families of mechanistic models (MM). Two classes of methodologies, based on Bayesian inference and Population of Models, currently prevail in parameter estimation for physical systems. However, in Bayesian analysis, uninformative priors for MM parameters introduce undesirable bias. Here, we propose how to infer parameters within the framework of stochastic inverse problems (SIP), also termed data-consistent inversion, wherein the prior targets only uncertainties that arise due to MM non-invertibility. To demonstrate, we introduce new methods to solve SIP based on rejection sampling, Markov chain Monte Carlo, and generative adversarial networks (GANs). In addition, to overcome limitations of SIP, we reformulate SIP based on constrained optimization and present a novel GAN to solve the constrained optimization problem.