Year |
Citation |
Score |
2020 |
Harmon JJ, Key C, Estep D, Butler T, Notaros BM. Adjoint-based Accelerated Adaptive Refinement in Frequency Domain 3-D Finite Element Method Scattering Problems Ieee Transactions On Antennas and Propagation. 1-1. DOI: 10.1109/Tap.2020.3016162 |
0.606 |
|
2020 |
Key C, Smull AP, Estep D, Butler T, Notaros BM. A Posteriori Error Estimation and Adaptive Discretization Refinement Using Adjoint Methods in CEM: A Study With a 1-D Higher Order FEM Scattering Example Ieee Transactions On Antennas and Propagation. 68: 3791-3806. DOI: 10.1109/Tap.2020.2976657 |
0.648 |
|
2020 |
Butler T, Wildey T, Yen TY. Data-consistent inversion for stochastic input-to-output maps Inverse Problems. 36: 85015. DOI: 10.1088/1361-6420/Ab8F83 |
0.518 |
|
2020 |
Butler TD, Jakeman JD, Wildey T. Optimal experimental design for prediction based on push-forward probability measures Journal of Computational Physics. 416: 109518. DOI: 10.1016/J.Jcp.2020.109518 |
0.47 |
|
2020 |
Butler T, Hakula H. What do we hear from a drum? A data-consistent approach to quantifying irreducible uncertainty on model inputs by extracting information from correlated model output data Computer Methods in Applied Mechanics and Engineering. 370: 113228. DOI: 10.1016/J.Cma.2020.113228 |
0.413 |
|
2019 |
He J, Mattis SA, Butler TD, Dawson CN. Data-driven uncertainty quantification for predictive flow and transport modeling using support vector machines Computational Geosciences. 23: 631-645. DOI: 10.1007/S10596-018-9762-4 |
0.529 |
|
2019 |
Mattis SA, Butler T. Enhancing piecewise‐defined surrogate response surfaces with adjoints on sets of unstructured samples to solve stochastic inverse problems International Journal For Numerical Methods in Engineering. 119: 923-940. DOI: 10.1002/Nme.6078 |
0.422 |
|
2018 |
Butler T, Wildey T. Utilizing Adjoint-Based Error Estimates For Surrogate Models To Accurately Predict Probabilities Of Events International Journal For Uncertainty Quantification. 8: 143-159. DOI: 10.1615/Int.J.Uncertaintyquantification.2018020911 |
0.388 |
|
2018 |
Butler TD, Jakeman JD, Wildey T. Convergence of Probability Densities Using Approximate Models for Forward and Inverse Problems in Uncertainty Quantification Siam Journal On Scientific Computing. 40. DOI: 10.1137/18M1181675 |
0.535 |
|
2018 |
Butler TD, Jakeman JD, Wildey T. Combining Push-Forward Measures and Bayes' Rule to Construct Consistent Solutions to Stochastic Inverse Problems Siam Journal On Scientific Computing. 40. DOI: 10.1137/16M1087229 |
0.59 |
|
2017 |
Panda N, Butler T, Estep D, Graham L, Dawson C. A Stochastic Inverse Problem For Multiscale Models International Journal For Multiscale Computational Engineering. 15: 265-283. DOI: 10.1615/Intjmultcompeng.2017020553 |
0.673 |
|
2017 |
Graham L, Butler T, Walsh S, Dawson C, Westerink JJ. A Measure-Theoretic Algorithm for Estimating Bottom Friction in a Coastal Inlet: Case Study of Bay St. Louis during Hurricane Gustav (2008) Monthly Weather Review. 145: 929-954. DOI: 10.1175/Mwr-D-16-0149.1 |
0.496 |
|
2017 |
Butler T, Graham L, Mattis S, Walsh S. A Measure-Theoretic Interpretation of Sample Based Numerical Integration with Applications to Inverse and Prediction Problems under Uncertainty Siam Journal On Scientific Computing. 39. DOI: 10.1137/16M1063289 |
0.494 |
|
2015 |
Butler T, Graham L, Estep D, Dawson C, Westerink JJ. Definition and solution of a stochastic inverse problem for the Manning's n parameter field in hydrodynamic models. Advances in Water Resources. 78: 60-79. PMID 25937695 DOI: 10.1016/J.Advwatres.2015.01.011 |
0.686 |
|
2015 |
Butler T, Huhtala A, Juntunen M. Quantifying uncertainty in material damage from vibrational data Journal of Computational Physics. 283: 414-435. DOI: 10.1016/J.Jcp.2014.12.011 |
0.414 |
|
2015 |
Mattis SA, Butler TD, Dawson CN, Estep D, Vesselinov VV. Parameter estimation and prediction for groundwater contamination based on measure theory Water Resources Research. DOI: 10.1002/2015Wr017295 |
0.661 |
|
2014 |
Butler TD, Estep DJ, Tavener S, Dawson C, Westerink JJ. A Measure-Theoretic Computational Method for Inverse Sensitivity Problems III: Multiple Quantities of Interest Siam/Asa Journal On Uncertainty Quantification. 2: 174-202. DOI: 10.1137/130930406 |
0.578 |
|
2014 |
Mayo T, Butler T, Dawson CN, Hoteit I. Data assimilation within the Advanced Circulation (ADCIRC) modeling framework for the estimation of Manning’s friction coefficient Ocean Modelling. 76: 43-58. DOI: 10.1016/J.Ocemod.2014.01.001 |
0.403 |
|
2013 |
Butler T, Estep D. A numerical method for solving a stochastic inverse problem for parameters. Annals of Nuclear Energy. 52. PMID 24347806 DOI: 10.1016/J.Anucene.2012.05.016 |
0.717 |
|
2013 |
Butler TD, Dawson C, Wildey T. Propagation of Uncertainties Using Improved Surrogate Models Siam/Asa Journal On Uncertainty Quantification. 1: 164-191. DOI: 10.1137/120888399 |
0.454 |
|
2012 |
Butler T, Estep D, Sandelin J. A COMPUTATIONAL MEASURE THEORETIC APPROACH TO INVERSE SENSITIVITY PROBLEMS II: A POSTERIORI ERROR ANALYSIS. Siam Journal On Numerical Analysis. 50: 22-45. PMID 23667271 DOI: 10.1137/100785958 |
0.687 |
|
2012 |
Butler T, Altaf MU, Dawson CN, Hoteit I, Luo X, Mayo T. Data Assimilation within the Advanced Circulation (ADCIRC) Modeling Framework for Hurricane Storm Surge Forecasting Monthly Weather Review. 140: 2215-2231. DOI: 10.1175/Mwr-D-11-00118.1 |
0.359 |
|
2012 |
Butler T, Constantine P, Wildey T. A Posteriori Error Analysis of Parameterized Linear Systems Using Spectral Methods Siam Journal On Matrix Analysis and Applications. 33: 195-209. DOI: 10.1137/110840522 |
0.447 |
|
2012 |
Butler T, Juntunen M. Reparameterization for statistical state estimation applied to differential equations Journal of Computational Physics. 231: 2641-2654. DOI: 10.1016/J.Jcp.2011.12.019 |
0.365 |
|
2011 |
Breidt J, Butler T, Estep D. A MEASURE-THEORETIC COMPUTATIONAL METHOD FOR INVERSE SENSITIVITY PROBLEMS I: METHOD AND ANALYSIS. Siam Journal On Numerical Analysis. 49: 1836-1859. PMID 23637467 DOI: 10.1137/100785946 |
0.691 |
|
2011 |
Butler T, Dawson C, Wildey T. A Posteriori Error Analysis of Stochastic Differential Equations Using Polynomial Chaos Expansions Siam Journal On Scientific Computing. 33: 1267-1291. DOI: 10.1137/100795760 |
0.499 |
|
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