# Upscaling Uncertainty with Dynamic Discrepancy for a Multi-scale Carbon Capture System

arXiv: https://arxiv.org/abs/1411.2578 · HTML: https://mlsanity.com/paper/1411.2578/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2014.

## Built on by (most cited first, 1 of 1)

- [Forward variable selection enables fast and accurate dynamic system identification with Karhunen-Lo`eve decomposed Gaussian processes](https://mlsanity.com/paper/2205.13676/) (2022)

## Abstract (arXiv)

Uncertainties from model parameters and model discrepancy from small-scale models impact the accuracy and reliability of predictions of large-scale systems. Inadequate representation of these uncertainties may result in inaccurate and overconfident predictions during scale-up to larger models. Hence multiscale modeling efforts must quantify the effect of the propagation of uncertainties during upscaling. Using a Bayesian approach, we calibrate a small-scale solid sorbent model to Thermogravimetric (TGA) data on a functional profile using chemistry-based priors. Crucial to this effort is the representation of model discrepancy, which uses a Bayesian Smoothing Splines (BSS-ANOVA) framework. We use an intrusive uncertainty quantification (UQ) approach by including the discrepancy function within the chemical rate expressions; resulting in a set of stochastic differential equations. Such an approach allows for easily propagating uncertainty by propagating the joint model parameter and discrepancy posterior into the larger-scale system of rate expressions. The broad UQ framework presented here may have far-reaching impact into virtually all areas of science where multiscale modeling is used.