# Active subspace-based dimension reduction for chemical kinetics applications with epistemic uncertainty

@article{Vohra2018ActiveSD, title={Active subspace-based dimension reduction for chemical kinetics applications with epistemic uncertainty}, author={Manav Vohra and Alen Alexanderian and Hayley Guy and Sankaran Mahadevan}, journal={Combustion and Flame}, year={2018} }

## 13 Citations

### Quantification of modeling uncertainties in turbulent flames through successive dimension reduction

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### A Distributed Active Subspace Method for Scalable Surrogate Modeling of Function Valued Outputs

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An error estimate is provided that quantifies errors due to active subspace projection and truncated KL expansion of the output of the model output and the numerical performance of the surrogate modeling approach is demonstrated with an application example from biotransport.

### Towards predictive combustion kinetic models: Progress in model analysis and informative experiments

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### A tangent linear approximation of the ignition delay time. I: Sensitivity to rate parameters

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### Dimensionality Reduction in Surrogate Modeling: A Review of Combined Methods

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A review of the current state-of-the-art dimensionality reduction and surrogate modeling methods is introduced with a discussion of their mathematical implications, applications, and limitations.

### Arrhenius.jl: A Differentiable Combustion SimulationPackage

- Computer Science
- 2021

The benefits of differentiable programming are demonstrated in efficient and accurate gradient computations, with applications in uncertainty quantification, kinetic model reduction, data assimilation, and model discovery.

### Kernel‐based active subspaces with application to computational fluid dynamics parametric problems using the discontinuous Galerkin method

- Computer ScienceInternational Journal for Numerical Methods in Engineering
- 2022

A kernel‐based nonlinear method is developed in a broader mathematical framework that contemplates also the reduction in parameter space of multivariate objective functions and is shown in the context of a parametric computational fluid dynamics application solved with the discontinuous Galerkin method.

### SpineNet-6mA: A Novel Deep Learning Tool for Predicting DNA N6-Methyladenine Sites in Genomes

- BiologyIEEE Access
- 2020

A novel deep learning based model based on a special architecture called SpinalNet is proposed for the prediction of DNA N6-methyladenine sites in rice genomes and produces better scores than existing models regarding all evaluation parameters.

### Core-collapse Supernovae: From Neutrino-driven 1D Explosions to Light Curves and Spectra

- PhysicsThe Astrophysical Journal
- 2021

We present bolometric and broadband light curves and spectra for a suite of core-collapse supernova models exploded self-consistently in spherical symmetry within the PUSH framework. We analyze broad…

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