Timothy Michael Wildey

Scientific Machine Learning

Author profile picture

Scientific Machine Learning

tmwilde@sandia.gov

(505) 844-0760

Sandia National Laboratories, New Mexico
 P.O. Box 5800
 Albuquerque, NM 87185-1318

Biography

Tim joined Sandia National Labs in January, 2011 following a postdoctoral fellowship at the University of Texas at Austin. His research interests are finite element and finite volume methods, discontinuous Galerkin methods, hybridized discretizations, a posteriori error analysis and estimation, uncertainty quantification, adjoint methods, multiphysics and multiscale problems, operator splitting and decomposition, computational fluid dynamics, shock-hydrodynamics, geomechanics, flow and transport in porous media, numerical linear algebra, domain decomposition, multilevel and multiscale preconditioners, and parallel computing.

Education

Publications

  • 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. https://doi.org/10.1615/int.j.uncertaintyquantification.2024050125 Publication ID: 124644
  • 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 Presenation]. https://doi.org/10.2172/2430535 Publication ID: 125820
  • Wildey, T. (2023). A Scalable Variational Approach for Solving Data-Consistent Stochastic Inverse Problems [Conference Presenation]. https://doi.org/10.2172/2431041 Publication ID: 127604
  • Wildey, T. (2023). Estimating the Error in Solutions to Stochastic Inverse Problems When Using Machine Learning Surrogates [Conference Presenation]. https://doi.org/10.2172/2431500 Publication ID: 129244
  • Bachman, W.B., Robbe, P., Montes de Zapiain, D., Wildey, T., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Presenation]. https://doi.org/10.2172/2431686 Publication ID: 129788
  • 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]. https://doi.org/10.2172/2431864 Publication ID: 130456
  • Yen, T.Y., Wildey, T., & Wildey, T. (2023). Measuring and Assessing Uncertainty in Data Fusion Algorithms [Conference Presenation]. https://doi.org/10.2172/2431894 Publication ID: 130568
  • Wildey, T. (2023). Optimal Experimental Design for Prediction Using Data Consistent Inversion [Conference Presenation]. https://doi.org/10.2172/2431897 Publication ID: 130580
  • Bachman, W.B., Robbe, P., Wildey, T., Montes de Zapiain, D., Lim, H., & Lim, H. (2023). The multifaceted nature of uncertainty in structure-property linkage with crystal plasticity finite element model [Conference Proceeding]. AIAA SciTech Forum and Exposition, 2023. https://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85200316050&origin=inward Publication ID: 131340
  • Bachman, W.B., 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). https://doi.org/10.1115/1.4054237 Publication ID: 80523
  • Bachman, W.B., Robbe, P., Wildey, T., Montes de Zapiain, D., Lim, H., & Lim, H. (2022). The multifaceted uncertainty nature of structure-property linkage with crystal plasticity finite element model [Conference Paper]. https://doi.org/10.2514/6.2023-0525 Publication ID: 121884
  • 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 Presenation]. https://doi.org/10.2172/2005979 Publication ID: 120652
  • Bachman, W.B., Wildey, T., Lim, H., & Lim, H. (2022). Uncertainty Quantification of Constitutive Models in Crystal Plasticity Finite Element Method [Conference Presenation]. https://doi.org/10.2172/2005454 Publication ID: 119252
  • Bachman, W.B., 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. https://doi.org/10.3389/fmats.2022.915254 Publication ID: 80842
  • 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. https://doi.org/10.2172/1885882 Publication ID: 80135
  • Yen, T.Y., Wildey, T., & Wildey, T. (2022). Constructing Data-consistent Solutions to Stochastic Inverse Problems with Sparse Observable Data [Conference Presenation]. https://doi.org/10.2172/2005248 Publication ID: 118452
  • Wildey, T. (2022). Estimating Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models [Conference Presenation]. https://doi.org/10.2172/2005273 Publication ID: 118548
  • Harper, G.B., Wildey, T., & Wildey, T. (2022). Data Compression Techniques for Large-Scale Memory-Bound Finite Element Applications [Conference Poster]. https://doi.org/10.2172/2005387 Publication ID: 118988
  • Bachman, W.B., Wildey, T., Lim, H., & Lim, H. (2022). Microstructure-Sensitive UQ for Materials Constitutive Models in Crystal Plasticity Finite Element Method [Conference Poster]. https://doi.org/10.2172/2004262 Publication ID: 116272
  • 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 Presenation]. https://doi.org/10.2172/2003926 Publication ID: 114984
  • Yen, T.Y., Wildey, T., & Wildey, T. (2022). Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data [Conference Presenation]. https://doi.org/10.2172/2003989 Publication ID: 115220
  • Reuter, B.W., Wildey, T., & Wildey, T. (2022). Synchronous and Asynchronous Time Integration for Multiscale Simulations Using Hybridized Finite Element Methods [Conference Presenation]. https://doi.org/10.2172/2004007 Publication ID: 115284
  • Reuter, B.W., Geraci, G., Wildey, T., Eldred, M., & Eldred, M. (2022). Multifidelity Uncertainty Quantification For Non-Deterministic Models [Conference Presenation]. https://doi.org/10.2172/2003426 Publication ID: 113032
  • Bachman, W.B., 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). https://doi.org/10.1007/s00158-021-03102-y Publication ID: 106364
  • Bachman, W.B., Wildey, T., & Wildey, T. (2022). Solving inverse problems in process-structure-property linkage with Gaussian process regression [Conference Presenation]. https://doi.org/10.2172/2002172 Publication ID: 109656
  • Yen, T.Y., Wildey, T., Butler, T., & Butler, T. (2022). Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion [Conference Poster]. https://doi.org/10.2172/2002240 Publication ID: 109924
  • 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 Presenation]. https://doi.org/10.2172/2002264 Publication ID: 110020
  • Reuter, B.W., Geraci, G., Wildey, T., & Wildey, T. (2022). Efficient Multifidelity Strategies for Uncertainty Quantification of Non-Deterministic Models [Conference Presenation]. https://doi.org/10.2172/2002277 Publication ID: 110072
  • 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 Publication ID: 76127
  • Wildey, T., Butler, T., Jakeman, J.D., Bachman, W.B., & Bachman, W.B. (2021). Solving Stochastic Inverse Problems for Property-Structure Relationships in Computational Materials Science [Conference Presenation]. https://doi.org/10.2172/1890916 Publication ID: 79535
  • Wildey, T., Butler, T., Jakeman, J.D., & Jakeman, J.D. (2021). Combining Measure Theory and Bayes? Rule to Solve a Stochastic Inverse Problem [Conference Presenation]. https://doi.org/10.2172/1877851 Publication ID: 78143
  • Bachman, W.B., 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]. https://doi.org/10.2172/1853874 Publication ID: 77392
  • Bachman, W.B., Wildey, T., & Wildey, T. (2021). Solving stochastic inverse problems for property-structure linkage using data-consistent inversion and ML [Conference Poster]. https://doi.org/10.2172/1848050 Publication ID: 77388
  • Bachman, W.B., 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. https://doi.org/10.1007/s11837-020-04432-w Publication ID: 71200
  • Bachman, W.B., Wildey, T., & Wildey, T. (2021). Solving Inverse Problems for Process-Structure Linkages Using Asynchronous Parallel Bayesian Optimization [Conference Presenation]. Minerals, Metals and Materials Series. https://doi.org/10.2172/1854075 Publication ID: 77439
  • Bachman, W.B., 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 Publication ID: 71189
  • Bachman, W.B., 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 Publication ID: 71190
  • 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). https://doi.org/10.1016/j.jcp.2020.109518 Publication ID: 73477
  • Bachman, W.B., 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). https://doi.org/10.1063/5.0015672 Publication ID: 73589
  • Wildey, T., Butler, T., Yen, T.Y., & Yen, T.Y. (2020). Data-consistent inversion for stochastic input-to-output maps. Inverse Problems, 36(8). https://doi.org/10.1088/1361-6420/ab8f83 Publication ID: 73040
  • Bachman, W.B., 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. https://doi.org/10.1016/j.actamat.2020.04.054 Publication ID: 73364
  • Bachman, W.B., Wildey, T., Rodgers, T., & Rodgers, T. (2020). On supervised and unsupervised deep learning applications for materials informatics [Conference Poster]. https://www.osti.gov/biblio/1812467 Publication ID: 74399
  • Bachman, W.B., 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). https://doi.org/10.1115/1.4046697 Publication ID: 73008
  • Bachman, W.B., 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 Publication ID: 73191
  • Bachman, W.B., Rodgers, T., Wildey, T., & Wildey, T. (2020). Reification of latent microstructures: On supervised unsupervised and semi-supervised deep learning applications for microstructures in materials informatics. https://doi.org/10.2172/1673174 Publication ID: 100232
  • Wildey, T., Bruder, L., Gee, M.W., & Gee, M.W. (2020). Data-consistent solutions to stochastic inverse problems using a probabilistic multi-fidelity method based on conditional densities. International Journal for Uncertainty Quantification, 10(5), pp. 399-424. https://doi.org/10.1615/int.j.uncertaintyquantification.2020030092 Publication ID: 103196
  • Bachman, W.B., Wildey, T., & Wildey, T. (2019). Materials informatics: data-driven materials design and uncertainty quantification perspectives [Conference Poster]. https://www.osti.gov/biblio/1643479 Publication ID: 66699
  • Wildey, T., Bruder, L., Bui-Thanh, T., Butler, T., Jakeman, J.D., Marvin, B., Bachman, W.B., Walsh, S., & Walsh, S. (2019). Moving Beyond Forward Simulation to Enable Data-informed Physics-based Predictions [Presentation]. https://www.osti.gov/biblio/1646273 Publication ID: 66318
  • 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 Publication ID: 64879
  • Bachman, W.B., 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 Publication ID: 70358
  • 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 Publication ID: 69562
  • Wildey, T., Bachman, W.B., & Bachman, W.B. (2019). Using High Performance Computing to Enable Data-informed Multiscale Modeling with Applications to Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1640837 Publication ID: 69247
  • Bachman, W.B., 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 Publication ID: 68628
  • Seidl, D.T., van Bloemen Waanders, B.G., 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), pp. 1-21. https://doi.org/10.1080/17415977.2019.1603222 Publication ID: 68059
  • Bachman, W.B., 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. https://doi.org/10.1016/j.wear.2018.12.081 Publication ID: 64352
  • Wildey, T. (2019). Exploiting Low-dimensional Structure to Efficiently Perform Stochastic Inference for Prediction [Conference Poster]. https://www.osti.gov/biblio/1601938 Publication ID: 67079
  • 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. https://doi.org/10.1137/18M1193505 Publication ID: 59303
  • Wildey, T., Butler, T., Jakeman, J.D., Bui-Thanh, T., Marvin, B., Bruder, L., & Bruder, L. (2019). Developing Scalable and Multi-fidelity Approaches for Push-forward Based Inference [Conference Poster]. https://www.osti.gov/biblio/1596420 Publication ID: 64514
  • Wildey, T., Gorodetsky, A.A., Belme, A.C., Shadid, J.N., & Shadid, J.N. (2019). Robust uncertainty quantification using response surface approximations of discontinuous functions. International Journal for Uncertainty Quantification, 9(5), pp. 415-437. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2019026974 Publication ID: 68000
  • Bachman, W.B., 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. https://doi.org/10.1115/DETC2019-97986 Publication ID: 70480
  • 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). https://doi.org/10.1137/18m1181675 Publication ID: 98520
  • 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 Publication ID: 59327
  • 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 Publication ID: 59876
  • Wildey, T. (2018). Overview of Forward and Inverse Uncertainty Quantification Methods [Conference Poster]. https://www.osti.gov/biblio/1581941 Publication ID: 63733
  • 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 Publication ID: 62972
  • 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 Publication ID: 63211
  • Wildey, T., Butler, T., Jakeman, J.D., Seidl, D.T., van Bloemen Waanders, B.G., & van Bloemen Waanders, B.G. (2018). Data-informed Multiscale Modeling of Additive Materials [Conference Poster]. https://www.osti.gov/biblio/1523778 Publication ID: 62297
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2018). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1525680 Publication ID: 61583
  • 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 Publication ID: 61612
  • 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). https://doi.org/10.1115/1.4037457 Publication ID: 56168
  • Roach, R.A., Jared, B.H., Cook, A.W., Keicher, D., van Bloemen Waanders, B.G., 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 Publication ID: 60282
  • Wildey, T., Butler, T., & Butler, T. (2018). Utilizing adjoint-based error estimates for surrogate models to accurately predict probabilities of events. International Journal for Uncertainty Quantification, 8(2), pp. 143-159. https://doi.org/10.1615/Int.J.UncertaintyQuantification.2018020911 Publication ID: 53856
  • 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. https://doi.org/10.1137/16m1087229 Publication ID: 55599
  • 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 Publication ID: 58335
  • 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 Publication ID: 58203
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2017). Adaptive Multiscale Modeling Using Generalized Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1513505 Publication ID: 57313
  • 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 Publication ID: 56716
  • 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 Publication ID: 56717
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2017). Multiscale Modeling Using Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1458195 Publication ID: 56724
  • Seidl, D.T., van Bloemen Waanders, B.G., Wildey, T., & Wildey, T. (2017). Multiscale Interfaces for Large Scale Optimization [Conference Poster]. https://www.osti.gov/biblio/1456522 Publication ID: 55715
  • van Bloemen Waanders, B.G., 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 Publication ID: 55236
  • Seidl, D.T., van Bloemen Waanders, B.G., 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 Publication ID: 54920
  • van Bloemen Waanders, B.G., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2017). Multiscale optimization under uncertainty [Presentation]. https://www.osti.gov/biblio/1458249 Publication ID: 55002
  • 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 Publication ID: 55046
  • 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 Publication ID: 47138
  • 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 Publication ID: 47222
  • 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. https://doi.org/10.1016/j.jcp.2016.04.062 Publication ID: 48127
  • 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 Publication ID: 50347
  • 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 Publication ID: 50793
  • Carnes, B., 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 Publication ID: 50012
  • Wildey, T., van Bloemen Waanders, B.G., Seidl, D.T., & Seidl, D.T. (2016). Uncertainty Quantification for Multiscale Mortar Methods [Conference Poster]. https://www.osti.gov/biblio/1368791 Publication ID: 50172
  • Wildey, T., van Bloemen Waanders, B.G., 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 Publication ID: 49573
  • van Bloemen Waanders, B.G., Wildey, T., Seidl, D.T., Li, H., & Li, H. (2016). Multiscale Optimization Under Uncertainty [Conference Poster]. https://www.osti.gov/biblio/1348106 Publication ID: 48990
  • Wildey, T., Cyr, E.C., Shadid, J.N., van Bloemen Waanders, B.G., 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 Publication ID: 48514
  • 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 Publication ID: 45366
  • 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 Publication ID: 45375
  • Wildey, T., Jakeman, J.D., & Jakeman, J.D. (2015). Adaptive Bayesian Inference for Prediction. https://doi.org/10.2172/1221574 Publication ID: 45595
  • 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 Publication ID: 44737
  • 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 Publication ID: 43836
  • Cyr, E.C., Shadid, J.N., Wildey, T., Hensinger, D.M., Robinson, A.C., Rider, W., Scovazzi, G., & Scovazzi, G. (2015). IMEX Lagrangian Methods [Conference Poster]. https://www.osti.gov/biblio/1253301 Publication ID: 43417
  • 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 Publication ID: 42861
  • Wildey, T., Shadid, J.N., Cyr, E.C., Constantine, P., & Constantine, P. (2015). Enabling Efficient Uncertainty Quantification Using Adjoint-based Techniques. https://doi.org/10.2172/1179153 Publication ID: 43275
  • 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 Publication ID: 42478
  • Rider, W., Love, E., Wildey, T., & Wildey, T. (2014). Replacing Discon.nuous Functions in Limiters with Smooth Ones [Presentation]. https://www.osti.gov/biblio/1241667 Publication ID: 38694
  • 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. https://doi.org/10.2172/1177048 Publication ID: 40814
  • 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 :. https://doi.org/10.2172/1177077 Publication ID: 41017
  • Swiler, L.P., Wildey, T., & Wildey, T. (2014). Sensitivity of precipitation to parameter values in the community atmosphere model version 5. https://doi.org/10.2172/1204103 Publication ID: 37049
  • 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 Publication ID: 40117
  • 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 Publication ID: 40175
  • Shadid, J.N., Pawlowski, R., Cyr, E.C., Wildey, T., & Wildey, T. (2014). Thermal hydraulic simulations, error estimation and parameter sensitivity studies in Drekar::CFD. https://doi.org/10.2172/1204072 Publication ID: 36835
  • 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 Publication ID: 36511
  • 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 Publication ID: 36063
  • 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 Publication ID: 36095
  • Jakeman, J.D., Wildey, T., & Wildey, T. (2013). Scalable Uncertainty Quantification Methods [Presentation]. https://www.osti.gov/biblio/1666168 Publication ID: 34628
  • 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 Publication ID: 33631
  • Jakeman, J.D., Wildey, T., & Wildey, T. (2013). Quantifying Uncertainty using a-posteriori Enhanced Sparse Grid Approximations [Conference]. https://www.osti.gov/biblio/1063316 Publication ID: 32169
  • 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 Publication ID: 32256
  • 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 Publication ID: 31415
  • Wildey, T. (2012). Error Control for Output QoIs Under Uncertainty [Conference]. https://www.osti.gov/biblio/1062303 Publication ID: 30554
  • 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 Publication ID: 30744
  • 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 Publication ID: 29105
  • Eldred, M., Wildey, T., & Wildey, T. (2012). Propagation of model form uncertainty for thermal hydraulics using RANS turbulence models in Drekar. https://doi.org/10.2172/1051699 Publication ID: 29125
  • 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 Publication ID: 29275
  • Wildey, T. (2012). Error Decomposition and Adaptive Refinement for Stochastic Differential Equations [Presentation]. https://www.osti.gov/biblio/1658506 Publication ID: 29331
  • 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 Publication ID: 28851
  • 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 Publication ID: 28017
  • 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 Publication ID: 27536
  • Pawlowski, R., Shadid, J.N., Wildey, T., & Wildey, T. (2011). Embedded UQ and QoI/Adjoints in Drekar: New Directions [Conference]. https://www.osti.gov/biblio/1113265 Publication ID: 25656
  • Wildey, T. (2011). Quantification and Reduction of Uncertainties for Regional Scale Climate Predictions [Presentation]. https://www.osti.gov/biblio/1662132 Publication ID: 24184
  • Wildey, T. (2011). Preconditioning for Mixed Finite Element Formulations of Elliptic Problems [Conference]. https://www.osti.gov/biblio/1106970 Publication ID: 22828
  • Wildey, T. (2011). Multiscale Mortar Methods for Flow and Mechanics in Porous Media [Conference]. https://www.osti.gov/biblio/1288934 Publication ID: 22560
  • Wildey, T. (2011). A Dirichlet-to-Neumann (DtN) multigrid algorithm for locally conservative methods [Conference]. https://www.osti.gov/biblio/1288632 Publication ID: 22028
Showing 10 of 132 publications.

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