Publications

Swiler, Laura P.; Eldred, Michael S.

Abstract not provided.

Computational and Mathematical Organization Theory

Naugle, Asmeret B.; Russell, Adam R.; Lakkaraju, Kiran L.; Swiler, Laura P.; Verzi, Stephen J.; Romero, Vicente J.

Social systems are uniquely complex and difficult to study, but understanding them is vital to solving the world’s problems. The Ground Truth program developed a new way of testing the research methods that attempt to understand and leverage the Human Domain and its associated complexities. The program developed simulations of social systems as virtual world test beds. Not only were these simulations able to produce data on future states of the system under various circumstances and scenarios, but their causal ground truth was also explicitly known. Research teams studied these virtual worlds, facilitating deep validation of causal inference, prediction, and prescription methods. The Ground Truth program model provides a way to test and validate research methods to an extent previously impossible, and to study the intricacies and interactions of different components of research.

Swiler, Laura P.

Abstract not provided.

Ivanoff, Thomas I.; Madison, Jonathan D.; Koepke, Joshua R.; Jared, Bradley H.; Mitchell, John A.; Swiler, Laura P.

Abstract not provided.

Ivanoff, Thomas I.; Madison, Jonathan D.; Koepke, Joshua R.; Jared, Bradley H.; Mitchell, John A.; Swiler, Laura P.

Abstract not provided.

Ivanoff, Thomas I.; Madison, Jonathan D.; Koepke, Joshua R.; Swaller, Erich S.; Jared, Bradley H.; Mitchell, John A.; Swiler, Laura P.

Abstract not provided.

Reliability Engineering and System Safety

Groth, Katrina G.; Swiler, Laura P.; Adams, Susan S.

In the past several years, several international agencies have begun to collect data on human performance in nuclear power plant simulators [1]. This data provides a valuable opportunity to improve human reliability analysis (HRA), but there improvements will not be realized without implementation of Bayesian methods. Bayesian methods are widely used in to incorporate sparse data into models in many parts of probabilistic risk assessment (PRA), but Bayesian methods have not been adopted by the HRA community. In this article, we provide a Bayesian methodology to formally use simulator data to refine the human error probabilities (HEPs) assigned by existing HRA methods. We demonstrate the methodology with a case study, wherein we use simulator data from the Halden Reactor Project to update the probability assignments from the SPAR-H method. The case study demonstrates the ability to use performance data, even sparse data, to improve existing HRA methods. Furthermore, this paper also serves as a demonstration of the value of Bayesian methods to improve the technical basis of HRA.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

Abstract not provided.

Romero, Vicente J.; Swiler, Laura P.; Urbina, Angel U.

This report discusses the treatment of uncertainties stemming from relatively few samples of random quantities. The importance of this topic extends beyond experimental data uncertainty to situations involving uncertainty in model calibration, validation, and prediction. With very sparse data samples it is not practical to have a goal of accurately estimating the underlying probability density function (PDF). Rather, a pragmatic goal is that the uncertainty representation should be conservative so as to bound a specified percentile range of the actual PDF, say the range between 0.025 and .975 percentiles, with reasonable reliability. A second, opposing objective is that the representation not be overly conservative; that it minimally over-estimate the desired percentile range of the actual PDF. The presence of the two opposing objectives makes the sparse-data uncertainty representation problem interesting and difficult. In this report, five uncertainty representation techniques are characterized for their performance on twenty-one test problems (over thousands of trials for each problem) according to these two opposing objectives and other performance measures. Two of the methods, statistical Tolerance Intervals and a kernel density approach specifically developed for handling sparse data, exhibit significantly better overall performance than the others.

Naugle, Asmeret B.; Lakkaraju, Kiran L.; Balanaga, Vamshi B.; Warrender, Christina E.; Verzi, Stephen J.; Livesay, Michael L.; Swiler, Laura P.

Abstract not provided.

Becker, D-A B.; Brooks, Dusty M.; Govaerts, J G.; Koskinen, L K.; Kucherenko, S K.; Kupiainen, P K.; Mariner, Paul M.; Pastina, B P.; Plischke, E P.; Rohlig, K-J R.; Sevougian, Stephen D.; Spiessl, S S.; Stein, Emily S.; Swiler, Laura P.; Weetjens, E W.

Abstract not provided.

Naugle, Asmeret B.; Lakkaraju, Kiran L.; Verzi, Stephen J.; Swiler, Laura P.; Romero, Vicente J.; Bernard, Michael L.

Abstract not provided.

Foiles, Stephen M.; Thompson, Aidan P.; Swiler, Laura P.; Trott, Christian R.

Abstract not provided.

Ray, Jaideep R.; Swiler, Laura P.; Huang, Maoyi H.; Hou, Zhangshuan H.

Abstract not provided.

Swiler, Laura P.; Ray, Jaideep R.

Abstract not provided.

Bao, Jie B.; Ren, huiying R.; Huang, Maoyi H.; Ray, Jaideep R.; Swiler, Laura P.

Abstract not provided.

Kamm, James R.; Weirs, Vincent G.; Swiler, Laura P.; Adams, Brian M.; Rider, William J.; Eldred, Michael S.

Abstract not provided.

Swiler, Laura P.; Romero, Vicente J.

Abstract not provided.

Gulian, Mamikon G.; Swiler, Laura P.; Frankel, Ari L.; Safta, Cosmin S.; Jakeman, John D.

Abstract not provided.

Gulian, Mamikon G.; Swiler, Laura P.; Frankel, Ari L.; Jakeman, John D.; Safta, Cosmin S.

Abstract not provided.

Swiler, Laura P.; Romero, Vicente J.

Abstract not provided.

Swiler, Laura P.; Wyss, Gregory D.

This document is a reference guide for the UNIX Library/Standalone version of the Latin Hypercube Sampling Software. This software has been developed to generate Latin hypercube multivariate samples. This version runs on Linux or UNIX platforms. This manual covers the use of the LHS code in a UNIX environment, run either as a standalone program or as a callable library. The underlying code in the UNIX Library/Standalone version of LHS is almost identical to the updated Windows version of LHS released in 1998 (SAND98-0210). However, some modifications were made to customize it for a UNIX environment and as a library that is called from the DAKOTA environment. This manual covers the use of the LHS code as a library and in the standalone mode under UNIX.

Modine, N.A.; Stephens, John A.; Swiler, Laura P.; Thompson, Aidan P.; Vogel, Dayton J.; Cangi, Attila C.; Feilder, Lenz F.; Rajamanickam, Sivasankaran R.

The focus of this project is to accelerate and transform the workflow of multiscale materials modeling by developing an integrated toolchain seamlessly combining DFT, SNAP, LAMMPS, (shown in Figure 1-1) and a machine-learning (ML) model that will more efficiently extract information from a smaller set of first-principles calculations. Our ML model enables us to accelerate first-principles data generation by interpolating existing high fidelity data, and extend the simulation scale by extrapolating high fidelity data (10² atoms) to the mesoscale (10⁴ atoms). It encodes the underlying physics of atomic interactions on the microscopic scale by adapting a variety of ML techniques such as deep neural networks (DNNs), and graph neural networks (GNNs). We developed a new surrogate model for density functional theory using deep neural networks. The developed ML surrogate is demonstrated in a workflow to generate accurate band energies, total energies, and density of the 298K and 933K Aluminum systems. Furthermore, the models can be used to predict the quantities of interest for systems with more number of atoms than the training data set. We have demonstrated that the ML model can be used to compute the quantities of interest for systems with 100,000 Al atoms. When compared with 2000 Al system the new surrogate model is as accurate as DFT, but three orders of magnitude faster. We also explored optimal experimental design techniques to choose the training data and novel Graph Neural Networks to train on smaller data sets. These are promising methods that need to be explored in the future.