# A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations

arXiv: https://arxiv.org/abs/2107.12182 · HTML: https://mlsanity.com/paper/2107.12182/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Jul 2021.

## Built on by (most cited first, 2 of 2)

- [A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty](https://mlsanity.com/paper/2403.08901/) (2024)
- [UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration](https://mlsanity.com/paper/2411.16693/) (2024)

## Abstract (arXiv)

This work develops a multiphase thermomechanical model of porous silica aerogel and implements an uncertainty analysis framework consisting of the Sobol methods for global sensitivity analyses and Bayesian inference using a set of experimental data of silica aerogel. A notable feature of this work is implementing a new noise model within the Bayesian inversion to account for data uncertainty and modeling error. The hyper-parameters in the likelihood balance data misfit and prior contribution to the parameter posteriors and prevent their biased estimation. The results indicate that the uncertainty in solid conductivity and elasticity are the most influential parameters affecting the model output variance. Also, the Bayesian inference shows that despite the microstructural randomness in the thermal measurements, the model captures the data with 2% error. However, the model is inadequate in simulating the stress-strain measurements resulting in significant uncertainty in the computational prediction of a building insulation component.