# UQ of 2D Slab Burner DNS: Surrogates, Uncertainty Propagation, and Parameter Calibration

arXiv: https://arxiv.org/abs/2411.16693 · HTML: https://mlsanity.com/paper/2411.16693/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 75 references that are in the map.
- First posted on arXiv 9 Nov 2024.

## Builds on (most cited first, 13 of 13)

- [Survey on Feature Selection](https://mlsanity.com/paper/1510.02892/) (2015)
- [Modern Monte Carlo Methods for Efficient Uncertainty Quantification and Propagation: A Survey](https://mlsanity.com/paper/2011.00680/) (2020)
- [A least-squares method for sparse low rank approximation of multivariate functions](https://mlsanity.com/paper/1305.0030/) (2013)
- [A machine learning approach for efficient uncertainty quantification using multiscale methods](https://mlsanity.com/paper/1711.04315/) (2017)
- [Bayesian Inference of Tissue Heterogeneity for Individualized Prediction of Glioma Growth](https://mlsanity.com/paper/2209.12089/) (2022)
- [A Framework for Strategic Discovery of Credible Neural Network Surrogate Models under Uncertainty](https://mlsanity.com/paper/2403.08901/) (2024)
- [Measurement of Hybrid Rocket Solid Fuel Regression Rate for a Slab Burner using Deep Learning](https://mlsanity.com/paper/2108.11276/) (2021)
- [On-site surrogates for large-scale calibration](https://mlsanity.com/paper/1810.01903/) (2018)
- [A Forward Backward Greedy approach for Sparse Multiscale Learning](https://mlsanity.com/paper/2102.07068/) (2021)
- [A Predictive Multiphase Model of Silica Aerogels for Building Envelope Insulations](https://mlsanity.com/paper/2107.12182/) (2021)
- [Combined Data and Deep Learning Model Uncertainties: An Application to the Measurement of Solid Fuel Regression Rate](https://mlsanity.com/paper/2210.14287/) (2022)
- [Unstructured Overlapping Mesh Distribution in Parallel](https://mlsanity.com/paper/1506.06194/) (2015)
- [An Ensemble-Based Deep Framework for Estimating Thermo-Chemical State Variables from Flamelet Generated Manifolds](https://mlsanity.com/paper/2211.14098/) (2022)

## Abstract (arXiv)

The goal of this paper is to demonstrate and address challenges related to all aspects of performing a complete uncertainty quantification analysis of a complicated physics-based simulation like a 2D slab burner direct numerical simulation (DNS). The UQ framework includes the development of data-driven surrogate models, propagation of parametric uncertainties to the fuel regression rate--the primary quantity of interest--and Bayesian calibration of the latent heat of sublimation and a chemical reaction temperature exponent using experimental data. Two surrogate models, a Gaussian Process (GP) and a Hierarchical Multiscale Surrogate (HMS) were constructed using an ensemble of 64 simulations generated via Latin Hypercube sampling. HMS is superior for prediction demonstrated by cross-validation and able to achieve an error < 15% when predicting multiscale boundary quantities just from a few far field inputs. Subsequent Bayesian calibration of chemical kinetics and fuel response parameters against experimental observations showed that the default values used in the DNS should be higher to better match measurements. This study highlights the importance of surrogate model selection and parameter calibration in quantifying uncertainty in predictions of fuel regression rates in complex combustion systems.