32 citations · 69 across the 9 of their papers we have counts for
10 papers
A Fully Convolutional Approach to Denoising 2D Correlation Spectra
Nisar Nellikunnummel, Andi M Barbour, Lutz Wiegart +2
We present a fully convolutional denoising autoencoder (FC-DAE) tailored for two-dimensional representations of dynamic correlations that is applicable to many experimental techniq…
Machine Learning Enhances Algorithms for Quantifying Non-Equilibrium Dynamics in Correlation Spectroscopy Experiments to Reach Frame-Rate-Limited Time Resolution
Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin +2
Analysis of X-ray Photon Correlation Spectroscopy (XPCS) data for non-equilibrium dynamics often requires manual binning of age regions of an intensity-intensity correlation functi…
Randomized Algorithms for Scientific Computing (RASC)
Aydin Buluc, Tamara G. Kolda, Stefan M. Wild +16
Randomized algorithms have propelled advances in artificial intelligence and represent a foundational research area in advancing AI for Science. Future advancements in DOE Office o…
Noise Reduction in X-ray Photon Correlation Spectroscopy with Convolutional Neural Networks Encoder-Decoder Models
Tatiana Konstantinova, Lutz Wiegart, Maksim Rakitin +2
Like other experimental techniques, X-ray Photon Correlation Spectroscopy is subject to various kinds of noise. Random and correlated fluctuations and heterogeneities can be presen…
Resource-Constrained Optimal Experimental Design
Anthony M. DeGennaro, Francis J. Alexander
The goal of this paper is to make Optimal Experimental Design (OED) computationally feasible for problems involving significant computational expense. We focus exclusively on the M…
Model Structural Inference using Local Dynamic Operators
Anthony M. DeGennaro, Nathan M. Urban, Balasubramanya T. Nadiga +1
This paper focuses on the problem of quantifying the effects of model-structure uncertainty in the context of time-evolving dynamical systems. This is motivated by multi-model unce…
Scalable Extended Dynamic Mode Decomposition using Random Kernel Approximation
Anthony M. DeGennaro, Nathan M. Urban
The Koopman operator is a linear, infinite-dimensional operator that governs the dynamics of system observables; Extended Dynamic Mode Decomposition (EDMD) is a data-driven method…
Uncertainty Quantification for Cargo Hold Fires
Anthony M. DeGennaro, Mark W. Lohry, Luigi Martinelli +1
The purpose of this study is twofold -- first, to introduce the application of high-order discontinuous Galerkin methods to buoyancy-driven cargo hold fire simulations, second, to…
Data-Driven Low-Dimensional Modeling and Uncertainty Quantification for Airfoil Icing
Anthony M. DeGennaro, Clarence W. Rowley, Luigi Martinelli
The formation and accretion of ice on the leading edge of an airfoil can be detrimental to aerodynamic performance. Furthermore, the geometric shape of leading edge ice profiles ca…
Uncertainty Quantification for Airfoil Icing using Polynomial Chaos Expansions
Anthony M. DeGennaro, Clarence W. Rowley, Luigi Martinelli
The formation and accretion of ice on the leading edge of a wing can be detrimental to airplane performance. Complicating this reality is the fact that even a small amount of uncer…