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Engineering and Technology

D-Index
51
Citations
17873
World Ranking
3751
National Ranking
1094

Overview

William L. Oberkampf is affiliated with Sandia National Laboratories in the United States. Their research spans several fields, with a particular focus on Decision Sciences and Computer Science. The main topics of their work include Probabilistic and Robust Engineering Design, Simulation Techniques and Applications, Advanced Multi-Objective Optimization Algorithms, Model Reduction and Neural Networks, Systems Engineering Methodologies and Applications, Software Reliability and Analysis Research, and Modeling and Simulation Systems.

The subfields of their study include Management Science and Operations Research, Statistics, Probability and Uncertainty, Computational Theory and Mathematics, Statistical and Nonlinear Physics, and Control and Systems Engineering.

Several papers reflect their contributions to areas connected to model validation, verification, and uncertainty quantification. Recent publications include:

  • Assessment of Model Validation, Calibration, and Prediction Approaches in the Presence of Uncertainty, 2022, Journal of Verification Validation and Uncertainty Quantification
  • Model validation hierarchies for connecting system design to modeling and simulation capabilities, 2023, Progress in Aerospace Sciences
  • Exploitation of a Validation Hierarchy for Modeling and Simulation, 2023, AIAA SCITECH 2023 Forum
  • Closure on the Discussion of "Models, Uncertainty, and the Sandia V&V Challenge Problem" (Hazelrigg, G. A., and Klutke, G. A., ASME J. Verif. Valid. Uncert., 2020, 5(1), p. 015501), 2020, Journal of Verification Validation and Uncertainty Quantification
  • Reviewer's Recognition, 2021, Journal of Verification Validation and Uncertainty Quantification

William L. Oberkampf has frequently collaborated with several co-authors, including Christopher J. Roy, James M. Luckring, Scott Shaw, R. E. Graves, and Nolan W. Whiting.

Their research has been published predominantly in venues such as the Journal of Verification Validation and Uncertainty Quantification, Progress in Aerospace Sciences, and the AIAA SCITECH Forum.

In addition to articles, William L. Oberkampf has contributed to the book titled Verification, Validation, and Uncertainty Quantification in Scientific Computing, published by Cambridge University Press in 2025.

Best Publications

  • Verification and Validation in Scientific Computing

    William L. Oberkampf;Christopher J. Roy

  • Verification and Validation in Computational Fluid Dynamics

    William L. Oberkampf;Timothy G. Trucano

  • Verification and validation.

    Timothy Guy Trucano;William L Oberkampf;Martin. Pilch

  • Verification, Validation, and Predictive Capability in Computational Engineering and Physics

    William L Oberkampf;Timothy G Trucano;Charles Hirsch

  • Error and uncertainty in modeling and simulation

    William L. Oberkampf;Sharon M. DeLand;Brian M. Rutherford;Kathleen V. Diegert

  • Challenge problems: uncertainty in system response given uncertain parameters

    William L. Oberkampf;Jon C. Helton;Cliff A. Joslyn;Steven F. Wojtkiewicz

  • Measures of agreement between computation and experiment: validation metrics

    William L. Oberkampf;Matthew F. Barone

  • An exploration of alternative approaches to the representation of uncertainty in model predictions

    Jon C. Helton;Jay D. Johnson;William Oberkampf

  • Calibration, validation, and sensitivity analysis: What's what

    Timothy G. Trucano;Laura Painton Swiler;Takera Igusa;William Oberkampf

  • Model validation and predictive capability for the thermal challenge problem

    Scott Ferson;William L. Oberkampf;Lev Ginzburg

  • Alternative representations of epistemic uncertainty

    Jon C. Helton;William Oberkampf

  • Mathematical representation of uncertainty

    William Oberkampf;Jon Helton;Kari Sentz

  • Verification and validation benchmarks

    William Louis Oberkampf;Timothy Guy Trucano

  • Experimental uncertainty estimation and statistics for data having interval uncertainty.

    Kreinovich, Vladik (Applied Biomathematics, Setauket, New York);Oberkampf, William Louis (Applied Biomathematics, Setauket, New York);Ginzburg, Lev (Applied Biomathematics, Setauket, New York);Ferson, Scott (Applied Biomathematics, Setauket, New York)

  • Dependence in probabilistic modeling, Dempster-Shafer theory, and probability bounds analysis.

    William Louis Oberkampf;W. Troy Tucker;Jianzhong Zhang;Lev Ginzburg

  • Issues in Computational Fluid Dynamics Code Verification and Validation

    William L. Oberkampf;Frederick G. Blottner

  • Summary from the epistemic uncertainty workshop: consensus amid diversity

    Scott Ferson;Cliff A. Joslyn;Jon C. Helton;William L. Oberkampf

  • A sampling-based computational strategy for the representation of epistemic uncertainty in model predictions with evidence theory

    J.C. Helton;J.D. Johnson;W.L. Oberkampf;C.B. Storlie

  • Representation of analysis results involving aleatory and epistemic uncertainty

    Jon C. Helton;Jay D. Johnson;William L. Oberkampf;Cédric J. Sallaberry

  • Experimental Methodology for Computational Fluid Dynamics Code Validation

    Daniel P. Aeschliman;William L. Oberkampf

  • An exploration of alternative approaches to the representation of uncertainty in model predictions.

    Jay Dean Johnson;William Louis Oberkampf;Jon Craig Helton

Frequent Co-Authors

Christopher J. Roy
Christopher J. Roy Virginia Tech
Jon C. Helton
Jon C. Helton Arizona State University
Holger Babinsky
Holger Babinsky University of Cambridge
Christopher L. Rumsey
Christopher L. Rumsey Langley Research Center
Vladik Kreinovich
Vladik Kreinovich The University of Texas at El Paso
Roger B. Nelsen
Roger B. Nelsen Lewis & Clark College

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