Publications

Debusschere, Bert D.; Jakeman, John D.; Chowdhary, Kamaljit S.; Safta, Cosmin S.; Sargsyan, Khachik S.; Rai, P.R.; Ghanem, R.G.; Knio, O.K.; La Maitre, O.L.; Winokur, J.W.; Li, G.L.; Ghattas, O.G.; Moser, R.M.; Simmons, C.S.; Alexanderian, A.A.; Gattiker, J.G.; Higdon, D.H.; Lawrence, E.L.; Bhat, S.B.; Marzouk, Y.M.; Bigoni, D.B.; Cui, T.C.; Parno, M.P.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Wills, Ann E.; Debusschere, Bert D.

Abstract not provided.

Robinson, Allen C.; Carpenter, John H.; Debusschere, Bert D.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.; Wills, Ann E.

Abstract not provided.

Najm, H.N.; Eldred, Michael S.; Debusschere, Bert D.; Chowdhary, Kamaljit S.; Jakeman, John D.; Safta, Cosmin S.; Sargsyan, Khachik S.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.; Wills, Ann E.; Drake, Richard R.; Rider, William J.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.; Wills, Ann E.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.; Wills, Ann E.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.; Wills, Ann E.

Abstract not provided.

Robinson, Allen C.; Drake, Richard R.; Carpenter, John H.; Wills, Ann E.; Rider, William J.; Debusschere, Bert D.

Abstract not provided.

Safta, Cosmin S.; Sargsyan, Khachik S.; Bambha, Ray B.; Michelsen, Hope A.; Debusschere, Bert D.; Najm, H.N.; van Bloemen Waanders, Bart G.

Abstract not provided.

Robinson, Allen C.; Debusschere, Bert D.; Carpenter, John H.

Abstract not provided.

Geraci, Gianluca G.; Swiler, Laura P.; Crussell, Jonathan C.; Debusschere, Bert D.

Abstract not provided.

Proceedings of the 3rd International Conference on Uncertainty Quantification in Computational Sciences and Engineering, UNCECOMP 2019

Geraci, Gianluca G.; Swiler, Laura P.; Crussell, Jonathan C.; Debusschere, Bert D.

Communication networks have evolved to a level of sophistication that requires computer models and numerical simulations to understand and predict their behavior. A network simulator is a software that enables the network designer to model several components of a computer network such as nodes, routers, switches and links and events such as data transmissions and packet errors in order to obtain device and network level metrics. Network simulations, as many other numerical approximations that model complex systems, are subject to the specification of parameters and operative conditions of the system. Very often the full characterization of the system and their input is not possible, therefore Uncertainty Quantification (UQ) strategies need to be deployed to evaluate the statistics of its response and behavior. UQ techniques, despite the advancements in the last two decades, still suffer in the presence of a large number of uncertain variables and when the regularity of the systems response cannot be guaranteed. In this context, multifidelity approaches have gained popularity in the UQ community recently due to their flexibility and robustness with respect to these challenges. The main idea behind these techniques is to extract information from a limited number of high-fidelity model realizations and complement them with a much larger number of a set of lower fidelity evaluations. The final result is an estimator with a much lower variance, i.e. a more accurate and reliable estimator can be obtained. In this contribution we investigate the possibility to deploy multifidelity UQ strategies to computer network analysis. Two numerical configurations are studied based on a simplified network with one client and one server. Preliminary results for these tests suggest that multifidelity sampling techniques might be used as effective tools for UQ tools in network applications.

International Journal for Uncertainty Quantification

Geraci, Gianluca G.; Crussell, Jonathan C.; Swiler, Laura P.; Debusschere, Bert D.

Network modeling is a powerful tool to enable rapid analysis of complex systems that can be challenging to study directly using physical testing. Two approaches are considered: emulation and simulation. The former runs real software on virtualized hardware, while the latter mimics the behavior of network components and their interactions in software. Although emulation provides an accurate representation of physical networks, this approach alone cannot guarantee the characterization of the system under realistic operative conditions. Operative conditions for physical networks are often characterized by intrinsic variability (payload size, packet latency, etc.) or a lack of precise knowledge regarding the network configuration (bandwidth, delays, etc.); therefore uncertainty quantification (UQ) strategies should be also employed. UQ strategies require multiple evaluations of the system with a number of evaluation instances that roughly increases with the problem dimensionality, i.e., the number of uncertain parameters. It follows that a typical UQ workflow for network modeling based on emulation can easily become unattainable due to its prohibitive computational cost. In this paper, a multifidelity sampling approach is discussed and applied to network modeling problems. The main idea is to optimally fuse information coming from simulations, which are a low-fidelity version of the emulation problem of interest, in order to decrease the estimator variance. By reducing the estimator variance in a sampling approach it is usually possible to obtain more reliable statistics and therefore a more reliable system characterization. Several network problems of increasing difficulty are presented. For each of them, the performance of the multifidelity estimator is compared with respect to the single fidelity counterpart, namely, Monte Carlo sampling. For all the test problems studied in this work, the multifidelity estimator demonstrated an increased efficiency with respect to MC.

Geraci, Gianluca G.; Swiler, Laura P.; Crussell, Jonathan C.; Debusschere, Bert D.

Abstract not provided.

Casey, Tiernan A.; Debusschere, Bert D.; Eldred, Michael S.; Geraci, Gianluca G.; Ghanem, Roger G.; Jakeman, John D.; Marzouk, Youssef M.; Najm, H.N.; Safta, Cosmin S.; Sargsyan, Khachik S.

Abstract not provided.

Computers and Fluids

Robinson, Allen C.; Berry, Robert D.; Carpenter, John H.; Debusschere, Bert D.; Drake, Richard R.; Mattsson, A.E.; Rider, William J.

Uncertainty quantification (UQ) deals with providing reasonable estimates of the uncertainties associated with an engineering model and propagating them to final engineering quantities of interest. We present a conceptual UQ framework for the case of shock hydrodynamics with Euler's equations where the uncertainties are assumed to lie principally in the equation of state (EOS). In this paper we consider experimental data as providing both data and an estimate of data uncertainty. We propose a specific Bayesian inference approach for characterizing EOS uncertainty in thermodynamic phase space. We show how this approach provides a natural and efficient methodology for transferring data uncertainty to engineering outputs through an EOS representation that understands and deals consistently with parameter correlations as sensed in the data.Historically, complex multiphase EOSs have been built utilizing tables as the delivery mechanism in order to amortize the cost of creation of the tables over many subsequent continuum scale runs. Once UQ enters into the picture, however, the proper operational paradigm for multiphase tables become much less clear. Using a simple single-phase Mie-Grüneisen model we experiment with several approaches and demonstrate how uncertainty can be represented. We also show how the quality of the tabular representation is of key importance. As a first step, we demonstrate a particular tabular approach for the Mie-Grüneisen model which when extended to multiphase tables should have value for designing a UQ-enabled shock hydrodynamic modeling approach that is not only theoretically sound but also robust, useful, and acceptable to the modeling community. We also propose an approach to separate data uncertainty from modeling error in the EOS. © 2012 Elsevier Ltd.

Robinson, Allen C.; Carpenter, John H.; Drake, Richard R.; Wills, Ann E.; Rider, William J.; Berry, Robert D.; Debusschere, Bert D.

Abstract not provided.

Mariner, Paul M.; Berg, Timothy M.; Debusschere, Bert D.; Eckert, Aubrey C.; Harvey, Jacob H.; LaForce, Tara; Leone, Rosemary C.; Mills, Melissa M.; Nole, Michael A.; Park, Heeho D.; Perry, F.V.; Seidl, Daniel T.; Swiler, Laura P.; Chang, Kyung W.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and highlevel nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (DOE 2012, Table 6; Sevougian et al. 2019). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media.

Mariner, Paul M.; Debusschere, Bert D.; Fukuyama, David E.; Harvey, Jacob H.; LaForce, Tara; Leone, Rosemary C.; Perry, Frank V.; Swiler, Laura P.; TACONI, ANNA M.

The Spent Fuel and Waste Science and Technology (SFWST) Campaign of the U.S. Department of Energy (DOE) Office of Nuclear Energy (NE), Office of Spent Fuel & Waste Disposition (SFWD) is conducting research and development (R&D) on geologic disposal of spent nuclear fuel (SNF) and high-level nuclear waste (HLW). A high priority for SFWST disposal R&D is disposal system modeling (Sassani et al. 2021). The SFWST Geologic Disposal Safety Assessment (GDSA) work package is charged with developing a disposal system modeling and analysis capability for evaluating generic disposal system performance for nuclear waste in geologic media. This report describes fiscal year (FY) 2022 advances of the Geologic Disposal Safety Assessment (GDSA) performance assessment (PA) development groups of the SFWST Campaign. The common mission of these groups is to develop a geologic disposal system modeling capability for nuclear waste that can be used to assess probabilistically the performance of generic disposal options and generic sites. The modeling capability under development is called GDSA Framework (pa.sandia.gov). GDSA Framework is a coordinated set of codes and databases designed for probabilistically simulating the release and transport of disposed radionuclides from a repository to the biosphere for post-closure performance assessment. Primary components of GDSA Framework include PFLOTRAN to simulate the major features, events, and processes (FEPs) over time, Dakota to propagate uncertainty and analyze sensitivities, meshing codes to define the domain, and various other software for rendering properties, processing data, and visualizing results.

Mariner, Paul M.; Swiler, Laura P.; Seidl, Daniel T.; Debusschere, Bert D.; Vo, Johnathan V.; Frederick, Jennifer M.

Abstract not provided.

Mariner, Paul M.; Swiler, Laura P.; Seidl, Daniel T.; Debusschere, Bert D.; Vo, Johnathan V.; Frederick, Jennifer M.

Abstract not provided.

Carpenter, John H.; Robinson, Allen C.; Debusschere, Bert D.

Abstract not provided.

Carpenter, John H.; Debusschere, Bert D.; Robinson, Allen C.

Abstract not provided.