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

• URL: http://arxiv.org/abs/2009.08267v3
• Date: Wed, 27 Sep 2023 16:45:02 GMT
• Title: Novel and flexible parameter estimation methods for data-consistent inversion in mechanistic modeling
• Authors: Timothy Rumbell, Jaimit Parikh, James Kozloski, and Viatcheslav Gurev
• Abstract summary: We introduce new methods to solve inverse problems (SIP) based on rejection sampling, Markov chain Monte Carlo, and generative adversarial networks (GANs) To overcome limitations of SIP, we reformulate SIP based on constrained optimization and present a novel GAN to solve the constrained optimization problem.
• Score: 0.13635858675752988
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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