Publications

Yen, T.Y., Wildey, T., Butler, T., Spence, R., & Spence, R. (2024). Stability and Convergence of Solutions to Stochastic Inverse Problems Using Approximate Probability Densities. International Journal for Uncertainty Quantification, 15(4). 10.1615/int.j.uncertaintyquantification.2024054008

Reuter, B.W., Geraci, G., Wildey, T., & Wildey, T. (2024). Multfidelity Estimators for Non-Deterministic Models [Conference Presentation]. 10.2172/2540436

Reuter, B.W., Geraci, G., Wildey, T., & Wildey, T. (2024). ANALYSIS OF THE CHALLENGES IN DEVELOPING SAMPLE-BASED MULTIFIDELITY ESTIMATORS FOR NONDETERMINISTIC MODELS. International Journal for Uncertainty Quantification, 14(5), pp. 1-30. 10.1615/int.j.uncertaintyquantification.2024050125

Wildey, T., Geraci, G., Reuter, B.W., & Reuter, B.W. (2023). The Approximate Control Variate Framework for Efficient Multifidelity Uncertainty Quantification of Non-Deterministic Models [Conference Presentation]. 10.2172/2430535

Foulk, J.W., Robbe, P., Lim, H., Wildey, T., de Zapiain, D.M., & de Zapiain, D.M. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presentation]. 10.2172/2540528

Wildey, T. (2023). A Scalable Variational Approach for Solving Data-Consistent Stochastic Inverse Problems [Conference Presentation]. 10.2172/2431041

Wildey, T. (2023). Estimating the Error in Solutions to Stochastic Inverse Problems When Using Machine Learning Surrogates [Conference Presentation]. 10.2172/2431500

Foulk, J.W., Robbe, P., de Zapiain, D.M., Wildey, T., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presentation]. 10.2172/2431686

Yen, T.Y., Wildey, T., & Wildey, T. (2023). Transferring Properties of Analogous Datasets Through Data-Informed Latent Spaces Using Heterogeneous Metaphoric Data Fusion [Conference Poster]. 10.2172/2431864

Yen, T.Y., Wildey, T., & Wildey, T. (2023). Measuring and Assessing Uncertainty in Data Fusion Algorithms [Conference Presentation]. 10.2172/2431894

White, R.D., Jakeman, J.D., Wildey, T., & Wildey, T. (2023). Using Data-Consistent Inversion to Build Population-Informed Priors for Bayesian Inference [Conference Presentation]. 10.2172/2540422

Wildey, T. (2023). Optimal Experimental Design for Prediction Using Data Consistent Inversion [Conference Presentation]. 10.2172/2431897

Foulk, J.W., Robbe, P., Wildey, T., de Zapiain, D.M., Lim, H., & Lim, H. (2022). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Proceeding]. https://www.osti.gov/biblio/2432135

Foulk, J.W., Sun, J., Liu, D., Wang, Y., Wildey, T., & Wildey, T. (2022). A Stochastic Reduced-Order Model for Statistical Microstructure Descriptors Evolution. Journal of Computing and Information Science in Engineering, 22(6). 10.1115/1.4054237

Foulk, J.W., Robbe, P., Wildey, T., de Zapiain, D.M., Lim, H., & Lim, H. (2022). The multifaceted uncertainty nature of structure-property linkage with crystal plasticity finite element model [Conference Paper]. 10.2514/6.2023-0525

Yen, T.Y., Wildey, T., & Wildey, T. (2022). Leveraging Physics-based Surrogates for Efficient Density Estimation of Sparse Observable Data on Low-dimensional Manifolds [Conference Presentation]. 10.2172/2005979

Foulk, J.W., Wildey, T., Lim, H., & Lim, H. (2022). Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method [Conference Presentation]. 10.2172/2005454

Foulk, J.W., Wildey, T., Lim, H., & Lim, H. (2022). Microstructure-Sensitive Uncertainty Quantification for Crystal Plasticity Finite Element Constitutive Models Using Stochastic Collocation Methods. Frontiers in Materials, 9. 10.3389/fmats.2022.915254

Eldred, M., Adams, B.M., Geraci, G., Portone, T., Ridgway, E.M., Stephens, J.A., Wildey, T., & Wildey, T. (2022). Deployment of Multifidelity Uncertainty Quantification for Thermal Battery Assessment Part I: Algorithms and Single Cell Results. 10.2172/1885882

Yen, T.Y., Wildey, T., & Wildey, T. (2022). Constructing Data-consistent Solutions to Stochastic Inverse Problems with Sparse Observable Data [Conference Presentation]. 10.2172/2005248

Wildey, T. (2022). Estimating Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models [Conference Presentation]. 10.2172/2005273

Harper, G.B., Wildey, T., & Wildey, T. (2022). Data Compression Techniques for Large-Scale Memory-Bound Finite Element Applications [Conference Poster]. 10.2172/2005387

Foulk, J.W., Wildey, T., Lim, H., & Lim, H. (2022). Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method [Conference Poster]. 10.2172/2004262

Wildey, T., Geraci, G., Eldred, M., Jakeman, J.D., Davis, O., Portone, T., Yen, T.Y., Reuter, B.W., Gorodetsky, A., Rushdi, A., Schiavazzi, D., Partin, L., & Partin, L. (2022). Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ [Conference Presentation]. 10.2172/2003926

Yen, T.Y., Wildey, T., & Wildey, T. (2022). Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data [Conference Presentation]. 10.2172/2003989

Reuter, B.W., Wildey, T., & Wildey, T. (2022). Synchronous and Asynchronous Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods [Conference Presentation]. 10.2172/2004007

Reuter, B.W., Geraci, G., Wildey, T., Eldred, M., & Eldred, M. (2022). Multifidelity Uncertainty Quantification For Non-Deterministic Models [Conference Presentation]. 10.2172/2003426

Foulk, J.W., Wildey, T., Furlan, J.M., Krishnan, P., Visintainer, R.J., McCann, S., & McCann, S. (2022). aphBO-2GP-3B: a budgeted asynchronous parallel multi-acquisition functions for constrained Bayesian optimization on high-performing computing architecture. Structural and Multidisciplinary Optimization, 65(4). 10.1007/s00158-021-03102-y

Foulk, J.W., Wildey, T., & Wildey, T. (2022). Solving inverse problems in process-structure-property linkage with Gaussian process regression [Conference Presentation]. 10.2172/2002172

Yen, T.Y., Wildey, T., Butler, T., & Butler, T. (2022). Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion [Conference Poster]. 10.2172/2002240

Wildey, T., Butler, T., Yen, T.Y., & Yen, T.Y. (2022). A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models [Conference Presentation]. 10.2172/2002264

Reuter, B.W., Geraci, G., Wildey, T., & Wildey, T. (2022). Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models [Conference Presentation]. 10.2172/2002277

Eldred, M., Geraci, G., Gorodetsky, A.A., Jakeman, J.D., Portone, T., Wildey, T., Rushdi, A., Seidl, D.T., & Seidl, D.T. (2021). The Dakota Project: Connecting the Pipeline from Uncertainty Quantification R&D to Mission Impact [Presentation]. https://www.osti.gov/biblio/1891078

Wildey, T., Butler, T., Jakeman, J.D., Foulk, J.W., & Foulk, J.W. (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science [Conference Presentation]. 10.2172/1890916

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2021). Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem [Conference Presentation]. 10.2172/1877851

Foulk, J.W., Tranchida, J., Wildey, T., Thompson, A.P., & Thompson, A.P. (2021). Multi-fidelity ML/UQ and Bayesian Optimization for Materials Design: Application to Ternary Random Alloys [Conference Poster]. 10.2172/1853874

Foulk, J.W., Wildey, T., & Wildey, T. (2021). Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization [Conference Presentation]. 10.2172/1854075

Foulk, J.W., Wildey, T., & Wildey, T. (2021). Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML [Conference Poster]. 10.2172/1848050

Foulk, J.W., Wildey, T., & Wildey, T. (2021). Solving Stochastic Inverse Problems for Property–Structure Linkages Using Data-Consistent Inversion and Machine Learning. JOM, 73(1), pp. 72-89. 10.1007/s11837-020-04432-w

Foulk, J.W., Wildey, T., & Wildey, T. (2020). Solving stochastic inverse problems for structure-property linkages using data-consistent inversion [Conference Paper]. https://www.osti.gov/biblio/1825594

Foulk, J.W., Wildey, T., & Wildey, T. (2020). Solving inverse problems for process-structure linkages using asynchronous parallel Bayesian optimization [Conference Paper]. https://www.osti.gov/biblio/1825595

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2020). Optimal experimental design for prediction based on push-forward probability measures. Journal of Computational Physics, 416(C). 10.1016/j.jcp.2020.109518

Foulk, J.W., Wildey, T., Tranchida, J., Thompson, A.P., & Thompson, A.P. (2020). Multi-fidelity machine-learning with uncertainty quantification and Bayesian optimization for materials design: Application to ternary random alloys. Journal of Chemical Physics, 153(7). 10.1063/5.0015672

Wildey, T., Butler, T., Yen, T.Y., & Yen, T.Y. (2020). Data-consistent inversion for stochastic input-to-output maps. Inverse Problems, 36(8). 10.1088/1361-6420/ab8f83

Foulk, J.W., Mitchell, J.A., Swiler, L.P., Wildey, T., & Wildey, T. (2020). An active learning high-throughput microstructure calibration framework for solving inverse structure–process problems in materials informatics. Acta Materialia, 194(C), pp. 80-92. 10.1016/j.actamat.2020.04.054

Foulk, J.W., Wildey, T., Rodgers, T.M., & Rodgers, T.M. (2020). On supervised and unsupervised deep learning applications for materials informatics [Conference Poster]. https://www.osti.gov/biblio/1812467

Foulk, J.W., Wildey, T., McCann, S., & McCann, S. (2020). sMF-BO-2CoGP: A sequential multi-fidelity constrained Bayesian optimization framework for design applications. Journal of Computing and Information Science in Engineering, 20(3). 10.1115/1.4046697

Foulk, J.W., Sun, J., Wang, Y., Liu, D., Wildey, T., & Wildey, T. (2020). Multiscale stochastic reduced-order model for uncertainty propagation using Fokker-Planck equation with microstructure evolution applications. arXiv preprint. https://www.osti.gov/biblio/1834331

Foulk, J.W., Rodgers, T.M., Wildey, T., & Wildey, T. (2020). Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. 10.2172/1673174

Foulk, J.W., Wildey, T., & Wildey, T. (2019). Materials informatics: data-driven materials design and uncertainty quantification perspectives [Conference Poster]. https://www.osti.gov/biblio/1643479

Wildey, T., Bruder, L., Bui-Thanh, T., Butler, T., Jakeman, J.D., Marvin, B., Foulk, J.W., Walsh, S., & Walsh, S. (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions [Presentation]. https://www.osti.gov/biblio/1646273

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2019). Convergence of Probability Densities using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1641989

Foulk, J.W., Wildey, T., McCann, S., & McCann, S. (2019). A sequential bi-fidelity constrained Bayesian optimization for design applications [Conference Poster]. https://www.osti.gov/biblio/1641565

Wildey, T., Butler, T., Jakeman, J.D., Bruder, L., & Bruder, L. (2019). Solving Stochastic Inverse Problems using Approximate Push-forward Densities based on a Multi-fidelity Monte Carlo Method [Conference Poster]. https://www.osti.gov/biblio/1641047

Wildey, T., Foulk, J.W., & Foulk, J.W. (2019). Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1640837

Foulk, J.W., Wang, Y., Wildey, T., & Wildey, T. (2019). A step towards a versatile Bayesian optimization: constrained asynchronous batch-parallel multi-fidelity and mixed-integer extensions [Conference Poster]. https://www.osti.gov/biblio/1640079

Seidl, D.T., van Bloemen Waanders, B., Wildey, T., & Wildey, T. (2019). Simultaneous inversion of shear modulus and traction boundary conditions in biomechanical imaging. Inverse Problems in Science and Engineering, 28(2). 10.1080/17415977.2019.1603222

Foulk, J.W., Furlan, J.M., Pagalthivarthi, K.V., Visintainer, R.J., Wildey, T., Wang, Y., & Wang, Y. (2019). WearGP: A computationally efficient machine learning framework for local erosive wear predictions via nodal Gaussian processes. Wear, 422-423(C), pp. 9-26. 10.1016/j.wear.2018.12.081

Wildey, T. (2019). Exploiting Low-dimensional Structure to Efficiently Perform Stochastic Inference for Prediction [Conference Poster]. https://www.osti.gov/biblio/1601938

Wildey, T., Muralikrishnan, S., Bui-Thanh, T., & Bui-Thanh, T. (2019). Unified geometric multigrid algorithm for hybridized high-order finite element methods. SIAM Journal on Scientific Computing, 41(5), pp. S172-S195. 10.1137/18M1193505

Foulk, J.W., Wildey, T., McCann, S., & McCann, S. (2019). SBF-BO-2CoGP: A sequential bi-fidelity constrained Bayesian optimization for design applications [Conference Poster]. Proceedings of the ASME Design Engineering Technical Conference. 10.1115/DETC2019-97986

Wildey, T., Butler, T., Jakeman, J.D., Bui-Thanh, T., Marvin, B., Bruder, L., & Bruder, L. (2018). Developing Scalable and Multi-fidelity Approaches for Push-forward Based Inference [Conference Poster]. https://www.osti.gov/biblio/1596420

Wildey, T., Gorodetsky, A., Belme, A., Shadid, J.N., & Shadid, J.N. (2018). Robust Uncertainty Quantification using Response Surface Approximations of Discontinuous Functions. International Journal for Uncertainty Quantification, 9(5). 10.1615/Int.J.UncertaintyQuantification.2019026974

Wildey, T., Bruder, L., Gee, M.W., & Gee, M.W. (2018). Data-Consistent Solutions to Stochastic Inverse Problems Using a Probabilistic Multi-Fidelity Method Based on Conditional Densities. International Journal for Uncertainty Quantification, 10(5). 10.1615/int.j.uncertaintyquantification.2020030092

Butler, T., Jakeman, J.D., Wildey, T., & Wildey, T. (2018). Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification. SIAM Journal on Scientific Computing, 40(5). 10.1137/18M1181675

Wildey, T., Marvin, B., Bui-Thanh, T., & Bui-Thanh, T. (2018). Scalable Approximations for the Consistent Bayes Method [Conference Poster]. https://www.osti.gov/biblio/1594647

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2018). The Consistent Bayesian Approach for Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1592669

Wildey, T. (2018). Overview of Forward and Inverse Uncertainty Quantification Methods [Conference Poster]. https://www.osti.gov/biblio/1581941

Wildey, T., Butler, T., Jakeman, J.D., Marvin, B., & Marvin, B. (2018). Consistent Bayesian Inference with Push-forward Measures: Scalable Implementations and Applications [Conference Poster]. https://www.osti.gov/biblio/1567819

Jakeman, J.D., Butler, T., Eldred, M., Geraci, G., Gorodetsky, A., Wildey, T., & Wildey, T. (2018). Adaptive multi-index collocation for quantifying uncertainty [Conference Poster]. https://www.osti.gov/biblio/1806541

Wildey, T., Butler, T., Jakeman, J.D., Seidl, D.T., van Bloemen Waanders, B., & van Bloemen Waanders, B. (2018). Data-informed Multiscale Modeling of Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1523778

Seidl, D.T., van Bloemen Waanders, B., Wildey, T., & Wildey, T. (2018). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1525680

Wildey, T., Butler, T., Jakeman, J.D., Walsh, S., & Walsh, S. (2018). Optimal Experimental Design for Prediction Using a Consistent Bayesian Approach [Conference Poster]. https://www.osti.gov/biblio/1507835

Walsh, S.N., Wildey, T., Jakeman, J.D., & Jakeman, J.D. (2018). Optimal experimental design using a consistent Bayesian approach. ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part B: Mechanical Engineering, 4(1). 10.1115/1.4037457

Roach, R.A., Jared, B.H., Cook, A., Keicher, D., van Bloemen Waanders, B., Swiler, L.P., Seidl, D.T., Wildey, T., Whetten, S.R., & Whetten, S.R. (2018). Born Qualified EAB Telecon [Presentation]. https://www.osti.gov/biblio/1514821

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2018). Combining push-forward measures and bayes' rule to construct consistent solutions to stochastic inverse problems. SIAM Journal on Scientific Computing, 40(2), pp. A984-A1011. 10.1137/16m1087229

Wildey, T., Butler, T., & Butler, T. (2017). Utilizing Adjoint-Based Error Estimates for Surrogate Models to Accurately Predict Probabilities of Events. International Journal for Uncertainty Quantification, 8(2). 10.1615/Int.J.UncertaintyQuantification.2018020911

Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2017). A Consistent Bayesian Approach for Solving Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1469097

Wildey, T., Jakeman, J.D., Butler, T., & Butler, T. (2017). Advancing Beyond Interpretive Simulation to Inference for Prediction [Conference Poster]. https://www.osti.gov/biblio/1467988

Wildey, T., van Bloemen Waanders, B., Seidl, D.T., & Seidl, D.T. (2017). Adaptive Multiscale Modeling Using Generalized Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1513505

Mayr, M., Cyr, E.C., Shadid, J.N., Pawlowski, R., Wildey, T., Scovazzi, G., Zeng, X., Phillips, E., Conde, S., & Conde, S. (2017). Implicit-Explicit (IMEX) Time Integration for CFD & Multi-Physics Problems [Presentation]. https://www.osti.gov/biblio/1458227

Mayr, M., Cyr, E.C., Shadid, J.N., Pawlowski, R., Wildey, T., Scovazzi, G., Zeng, X., Phillips, E., Conde, S., & Conde, S. (2017). Implicit-Explicit (IMEX) Time Integration for Multi-Physics: Application to ALE-based CFD Simulations [Conference Poster]. https://www.osti.gov/biblio/1458228

Wildey, T., van Bloemen Waanders, B., Seidl, D.T., & Seidl, D.T. (2017). Multiscale Modeling Using Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1458195

Seidl, D.T., van Bloemen Waanders, B., Wildey, T., & Wildey, T. (2017). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1456522

van Bloemen Waanders, B., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2017). Multiscale optimization under uncertainty for additive manufacturing [Conference Poster]. https://www.osti.gov/biblio/1426379

Seidl, D.T., van Bloemen Waanders, B., Wildey, T., & Wildey, T. (2017). Simultaneous Estimation of Material Parameters and Neumann Boundary Conditions in a Linear Elastic Model by PDE-Constrained Optimization [Conference Poster]. https://www.osti.gov/biblio/1458297

van Bloemen Waanders, B., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2017). Multiscale optimization under uncertainty [Presentation]. https://www.osti.gov/biblio/1458249

Wildey, T., Jakeman, J.D., Butler, T., & Butler, T. (2017). Efficient Sampling Strategies for the Consistent Bayesian Approach for Solving Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1425298

Wildey, T., Shadid, J.N., Cyr, E.C., Chaudry, J., & Chaudry, J. (2016). Adjoint Enhanced Methods for Uncertainty Quantification of Multiphysics Applications [Conference Poster]. https://www.osti.gov/biblio/1400047

Cyr, E.C., Shadid, J.N., Wildey, T., Phillips, E., Robinson, A.C., Miller, S., Pawlowski, R., & Pawlowski, R. (2016). Implicit-Explicit (IMEX) Time Integration for Multi-Physics: Application to ALE and Plasma Simulation [Conference Poster]. https://www.osti.gov/biblio/1401944

Smith, T.M., Shadid, J.N., Cyr, E.C., Pawlowski, R., Wildey, T., & Wildey, T. (2016). Stabilized FE simulation of prototype thermal-hydraulics problems with integrated adjoint-based capabilities. Journal of Computational Physics, 321(C), pp. 321-341. 10.1016/j.jcp.2016.04.062

Wildey, T., Jakeman, J.D., Butler, T., & Butler, T. (2016). A Consistent Bayesian Approach for Stochastic Inverse Problems [Conference Poster]. https://www.osti.gov/biblio/1368940

Wildey, T., Shadid, J.N., Cyr, E.C., & Cyr, E.C. (2016). Adjoint Enhanced Methods for Uncertainty Quantification of Multiphysics Applications [Conference Poster]. https://www.osti.gov/biblio/1367627

Carnes, B.R., Bova, S.W., Fisher, T.C., Weirs, G., Wildey, T., & Wildey, T. (2016). Discretization error transport for unstructured CFD [Conference Poster]. https://www.osti.gov/biblio/1422082

Wildey, T., van Bloemen Waanders, B., Seidl, D.T., & Seidl, D.T. (2016). Uncertainty Quantification for Multiscale Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1368791

Wildey, T., van Bloemen Waanders, B., Seidl, D.T., Arbogast, T., Ganis, B., Girault, V., Pencheva, G., Wheeler, M.F., Xue, G., Yotov, I., Tavener, S., Vohralik, M., & Vohralik, M. (2016). Multiscale Mortar Methods: Theory Applications and Future Directions [Conference Poster]. https://www.osti.gov/biblio/1365248

van Bloemen Waanders, B., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2016). Multiscale Optimization Under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1348106

Wildey, T., Cyr, E.C., Shadid, J.N., van Bloemen Waanders, B., Kouri, D.P., Bishop, J.E., Tavener, S., Butler, T., Prudhomme, S., Dawson, C., & Dawson, C. (2016). Utilizing Adjoint-Based Techniques to Improve the Accuracy and Reliability in Uncertainty Quantification [Conference Poster]. https://www.osti.gov/biblio/1345103

Phipps, E.T., Red-Horse, J.R., Wildey, T., Constantine, P., Ghanem, R., Arnst, M., & Arnst, M. (2015). Stochastic Dimension Reduction of Multiphysics Systems through Measure Transformation [Conference Poster]. https://www.osti.gov/biblio/1321810

Wildey, T., Shadid, J.N., Cyr, E.C., Jakeman, J.D., Butler, T., & Butler, T. (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Transient Nonlinear Problems with Discontinuous Solutions [Conference Poster]. https://www.osti.gov/biblio/1323036

Wildey, T., Jakeman, J.D., & Jakeman, J.D. (2015). Adaptive Bayesian Inference for Prediction. 10.2172/1221574

Wildey, T., Jakeman, J.D., Butler, T., Cyr, E.C., Shadid, J.N., & Shadid, J.N. (2015). Adjoint-Based a Posteriori Error Estimation and Uncertainty Quantification for Shock-Hydrodynamic Applications [Conference Poster]. https://www.osti.gov/biblio/1279685

Wildey, T., Jakeman, J.D., Butler, T., & Butler, T. (2015). Utilizing Adjoint-based Error Estimates to Adaptively Resolve Response Surface Approximations [Conference Poster]. https://www.osti.gov/biblio/1256570

Cyr, E.C., Shadid, J.N., Wildey, T., Hensinger, D.M., Robinson, A.C., Rider, W.J., Scovazzi, G., & Scovazzi, G. (2015). IMEX Lagrangian Methods [Conference Poster]. https://www.osti.gov/biblio/1253301

Fisher, T.C., Bova, S.W., Weirs, G., Wildey, T., & Wildey, T. (2015). Propagating Discretization Error Estimates in Compressible Turbulent Flow Simulations [Presentation]. https://www.osti.gov/biblio/1246879

Wildey, T., Shadid, J.N., Cyr, E.C., Constantine, P., & Constantine, P. (2015). Enabling Efficient Uncertainty Quantification Using Adjoint-based Techniques. 10.2172/1179153

Wildey, T., Cyr, E.C., Shadid, J.N., & Shadid, J.N. (2015). Utilizing Adjoint-Based Techniques to Effectively Perform UQ on Discontinuous Responses [Conference Poster]. https://www.osti.gov/biblio/1246277

Rider, W.J., Love, E., Wildey, T., & Wildey, T. (2014). Replacing Discon.nuous Functions in Limiters with Smooth Ones [Presentation]. https://www.osti.gov/biblio/1241667

Adams, B.M., Jakeman, J.D., Swiler, L.P., Stephens, J.A., Vigil, D., Wildey, T., Bauman, L.E., Bohnhoff, W.J., Dalbey, K., Eddy, J.P., Ebeida, M., Eldred, M., Hough, P.D., Hu, K., & Hu, K. (2014). Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis version 6.0 theory manual. 10.2172/1177048

Adams, B.M., Jakeman, J.D., Swiler, L.P., Stephens, J.A., Vigil, D., Wildey, T., Bauman, L.E., Bohnhoff, W.J., Dalbey, K., Eddy, J.P., Ebeida, M., Eldred, M., Hough, P.D., Hu, K., & Hu, K. (2014). Dakota, a multilevel parallel object-oriented framework for design optimization, parameter estimation, uncertainty quantification, and sensitivity analysis :. 10.2172/1177077

Wildey, T. (2014). Error decomposition and adaptivity for response surface approximations from PDEs with parametric uncertainty. SIAM/ASA Journal on Uncertainty Quantification. https://www.osti.gov/biblio/1141195

Swiler, L.P., Wildey, T., & Wildey, T. (2014). Sensitivity of Precipitation to Parameter Values in the Community Atmosphere Model Version 5. 10.2172/1204103

Eldred, M., Wildey, T., Dowding, K.J., & Dowding, K.J. (2014). Advanced UQ/QMU Methods for Abnormal Thermal Safety Studies [Presentation]. https://www.osti.gov/biblio/1692314

Shadid, J.N., Pawlowski, R., Cyr, E.C., Wildey, T., & Wildey, T. (2013). Thermal Hydraulic Simulations, Error Estimation and Parameter Sensitivity Studies in Drekar::CFD. 10.2172/1204072

Eldred, M., Jakeman, J.D., Wildey, T., & Wildey, T. (2013). Deployment of Scalable UQ Methods for High-Fidelity Simulation-based Applications within the DOE [Presentation]. https://www.osti.gov/biblio/1673675

Wildey, T., Shadid, J.N., Cyr, E.C., & Cyr, E.C. (2013). Adjoint-Based Error Analysis for Stabilized Formulations and IMEX Schemes [Conference]. https://www.osti.gov/biblio/1140447

Shadid, J.N., Pawlowski, R., Cyr, E.C., Wildey, T., Weber, P.D., & Weber, P.D. (2013). Drekar::CFD-A Trilinos Component-Based Software Flow Solver [Conference]. https://www.osti.gov/biblio/1296709

Jakeman, J.D., Wildey, T., & Wildey, T. (2013). Scalable Uncertainty Quantification Methods [Presentation]. https://www.osti.gov/biblio/1666168

Shadid, J.N., Wildey, T., Pawlowski, R., Smith, T.M., & Smith, T.M. (2013). Error Estimation with Adjoints in Drekar: Applications to Fluid Flow and Magnetohydrodynamics Models [Presentation]. https://www.osti.gov/biblio/1661289

Red-Horse, J.R., Wildey, T., & Wildey, T. (2013). Stochastic Dimension Reduction of Multiphysics Systems through Measure Transformation [Conference]. https://www.osti.gov/biblio/1145287

Jakeman, J.D., Wildey, T., & Wildey, T. (2013). Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations [Conference]. https://www.osti.gov/biblio/1063316

Phipps, E.T., Wildey, T., & Wildey, T. (2013). Efficient uncertainty propagation for network multiphysics systems. Proposed for publication in International Journal for Numerical Methods in Engineering.. https://www.osti.gov/biblio/1063360

Wildey, T. (2012). Error Control for Output QoIs Under Uncertainty [Conference]. https://www.osti.gov/biblio/1062303

Wildey, T., Swiler, L.P., & Swiler, L.P. (2012). Progress on Calibration of 2-Degree Simulations from CAM5 [Presentation]. https://www.osti.gov/biblio/1648264

Jakeman, J.D., Wildey, T., Eldred, M., & Eldred, M. (2012). Adaptive sparse grids for uncertainty quantication Enhancing approximations using a posteriori error estimation [Conference]. https://www.osti.gov/biblio/1073416

Eldred, M., Wildey, T., & Wildey, T. (2012). Propagation of model form uncertainty for thermal hydraulics using RANS turbulence models in Drekar. 10.2172/1051699

Wildey, T., Shadid, J.N., & Shadid, J.N. (2012). Goal-oriented a posteriori analysis of stabilized finite element methods [Conference]. https://www.osti.gov/biblio/1073289

Wildey, T. (2012). Error Decomposition and Adaptive Refinement for Stochastic Differential Equations [Presentation]. https://www.osti.gov/biblio/1658506

Wildey, T. (2012). PROPAGATION OF UNCERTAINTIES USING IMPROVED SURROGATE MODELS. Proposed for publication in SIAM Journal on Uncertainty Quantification.. https://www.osti.gov/biblio/1064108

Wildey, T. (2012). A posteriori Analysis of Interior Penalty Discontinuous Galerkin Methods. Proposed for publication in SIAM Journal of Numerical Analysis.. https://www.osti.gov/biblio/1067830

Red-Horse, J.R., Wildey, T., & Wildey, T. (2012). Stochastic Dimension Reduction Techniques for Uncertainty Quantification of Multiphysics Systems [Conference]. https://www.osti.gov/biblio/1117584

Pawlowski, R., Shadid, J.N., Smith, T.M., Wildey, T., & Wildey, T. (2011). Embedded UQ and QoI/Adjoints in Drekar: New Directions [Conference]. https://www.osti.gov/biblio/1113265

Wildey, T., Phipps, E.T., Constantine, P.G., & Constantine, P.G. (2011). A Posteriori Error Analysis of Stochastic Differential Equations Using Polynomial Chaos Approximations [Presentation]. https://www.osti.gov/biblio/1106168

Wildey, T. (2011). Quantification and Reduction of Uncertainties for Regional Scale Climate Predictions [Presentation]. https://www.osti.gov/biblio/1662132

Wildey, T. (2011). Preconditioning for Mixed Finite Element Formulations of Elliptic Problems [Conference]. https://www.osti.gov/biblio/1106970

Wildey, T. (2011). Multiscale Mortar Methods for Flow and Mechanics in Porous Media [Conference]. https://www.osti.gov/biblio/1288934

Wildey, T. (2011). A Dirichlet-to-Neumann (DtN) multigrid algorithm for locally conservative methods [Conference]. https://www.osti.gov/biblio/1288632