Michael D. Shields

发表

Targeted random sampling for time-invariant reliability analysis

Michael D. Shields, V. S. Sundar, V. Sundar, 2015 .

Efficient Monte Carlo resampling for probability measure changes from Bayesian updating

Michael D. Shields, Jiaxin Zhang, M. Shields, 2019, Probabilistic Engineering Mechanics.

Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold pdf

Michael D. Shields, Dimitris G. Giovanis, 2020, ArXiv.

On the usefulness of gradient information in surrogate modeling: Application to uncertainty propagation in composite material models

Michael D. Shields, Philippe H. Geubelle, Anindya Bhaduri, 2020 .

Efficient global sensitivity analysis of structural vibration for a nuclear reactor system subject to nonstationary loading

John C. Brigham, Michael D. Shields, Gregory A. Banyay, 2020, Nuclear Engineering and Design.

Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations

Michael D. Shields, Dimitris G. Giovanis, D. G. Giovanis, 2017, J. Comput. Phys..

Some effects on information load on search patterns used to analyze performance reports

Michael D. Shields, Michael D. Shields, 1980 .

The design and effects of control systems: tests of direct- and indirect-effects models

Michael D. Shields, F. Johnny Deng, Yutaka Kato, 2000 .

Simulation of Spatially Correlated Nonstationary Response Spectrum–Compatible Ground Motion Time Histories

Michael D. Shields, M. Shields, 2015 .

Efficient global sensitivity analysis for flow-induced vibration of a nuclear reactor assembly using Kriging surrogates

John C. Brigham, Michael D. Shields, Gregory A. Banyay, 2019, Nuclear Engineering and Design.

Manufacturing controls and performance: An experiment

S. Mark Young, Gerrit Wolf, Michael D. Shields, 1988 .

Efficient uncertainty propagation for high-fidelity simulations with large parameter spaces: Application to stiffened plate buckling

Nicholas Reynolds, Michael D. Shields, Ken Nahshon, 2018 .

Bootstrap Monte Carlo method utilizing a refined stratified sampling routine for uncertainty quantification of very large transient dynamic computational models

Adam Hapij, Michael D. Shields, Kirubel Teferra, 2014 .

Uncertainty Propagation in Composite Models Using an Efficient Surrogate Algorithm

Michael D. Shields, Philippe H. Geubelle, Anindya Bhaduri, 2018 .

Discussion of Feng et al. (2014). Statistical reconstruction of two-phase random media Comput. Struct. 137 (2014) 78-92

Paolo Bocchini, George Deodatis, Michael D. Shields, 2016 .

Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification

Adam Hapij, Michael D. Shields, Kirubel Teferra, 2015, Reliab. Eng. Syst. Saf..

Mapping model validation metrics to subject matter expert scores for model adequacy assessment

Adam Hapij, Michael D. Shields, Kirubel Teferra, 2014, Reliab. Eng. Syst. Saf..

On the quantification and efficient propagation of imprecise probabilities with copula dependence

Michael D. Shields, Jiaxin Zhang, 2020, Int. J. Approx. Reason..

Surrogate-enhanced stochastic search algorithms to identify implicitly defined functions for reliability analysis

Michael D. Shields, V. S. Sundar, V. Sundar, 2016 .

Simulation of Stochastic Processes: Applications in Civil Engineering

Michael D. Shields, 2010 .

The generalization of Latin hypercube sampling

Michael D. Shields, Jiaxin Zhang, Jiaxin Zhang, 2015, Reliab. Eng. Syst. Saf..

Imprecise global sensitivity analysis using bayesian multimodel inference and importance sampling

Michael D. Shields, Jiaxin Zhang, Stephanie C. TerMaath, 2021 .

Cost Knowledge and Cost‐Based Judgment Performance

Michael D. Shields, David T. Dearman, Michael D. Shields, 2001 .

Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems

Michael D. Shields, M. Shields, 2018, Reliab. Eng. Syst. Saf..

Antecedents and Consequences of Standard-Based Control Systems

Michael D. Shields, Yutaka Kato, F. Johnny Deng, 1996 .

Intuitive Time-Series Extrapolation of Sales

James M. Peters, Michael D. Shields, 1994 .

UQpy: A general purpose Python package and development environment for uncertainty quantification

Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, 2020, J. Comput. Sci..

Reliability Analysis Using Adaptive Kriging Surrogates with Multimodel Inference

Michael D. Shields, V. S. Sundar, V. Sundar, 2019, ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering.

Simulation of non-Gaussian, non-stationary stochastic processes with evolutionary power

George Deodatis, Michael D. Shields, Brett A. Benowitz, 2013 .

Budgeting, Psychological Contracts, and Budgetary Misreporting

Michael D. Shields, Susanna Gallani, Ranjani Krishnan, 2019, Manag. Sci..

Topology optimization of linear structural system under stationary stochastic excitation

Mu Zhu, Yang Yang, James K. Guest, 2015 .

Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis

Robert Michael Kirby, Michael D. Shields, Yanyan He, 2017, J. Comput. Phys..

An empirical inquiry into the effects of information load on performance report analysis processes

Michael D. Shields, 1978 .

Mapping Management Accounting: Graphics and Guidelines for Theory-Consistent Empirical Research

Michael D. Shields, Joan L. Luft, Michael D. Shields, 2003 .

Subset simulation for problems with strongly non-Gaussian, highly anisotropic, and degenerate distributions

Michael D. Shields, Dimitris G. Giovanis, V.S. Sundar, 2021 .

3rd-order Spectral Representation Method: Simulation of multi-dimensional random fields and ergodic multi-variate random processes with fast Fourier transform implementation

Michael D. Shields, Lohit Vandanapu, Lohit Vandanapu, 2021 .

Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation

Benjamin W. Spencer, Michael D. Shields, Zachary M. Prince, 2022, Reliab. Eng. Syst. Saf..

Data-driven Uncertainty Quantification in Computational Human Head Models

Aaron Carass, K. T. Ramesh, Curtis L. Johnson, 2021 .

Variance-based adaptive sequential sampling for Polynomial Chaos Expansion

Miroslav Vořechovský, Michael D. Shields, Václav Sadílek, 2021 .

Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models pdf

Michael D. Shields, Dimitrios Loukrezis, Katiana Kontolati, 2021, ArXiv.

Grassmannian diffusion maps based surrogate modeling via geometric harmonics

Michael D. Shields, Dimitrios Loukrezis, Dimitris G. Giovanis, 2021, International Journal for Numerical Methods in Engineering.

Simulation of non-stationary and non-Gaussian random processes by 3rd-order Spectral Representation Method: Theory and POD implementation

Michael D. Shields, Lohit Vandanapu, Lohit Vandanapu, 2021, Mechanical Systems and Signal Processing.

A direct simulation method and lower-bound estimation for a class of gamma random fields with applications in modelling material properties

Michael D. Shields, Yong Liu, Yong Liu, 2017 .

Simulations of Ductile Fracture in an Idealized Ship Grounding Scenario Using Phenomenological Damage and Cohesive Zone Models

Michael D. Shields, Pawel Woelke, Najib N. Abboud, 2013 .

Topology optimization for linear stationary stochastic dynamics: Applications to frame structures

Yang Yang, James K. Guest, Michael D. Shields, 2017 .

A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic process by a translation process

Paolo Bocchini, George Deodatis, Michael D. Shields, 2011 .

Targeted random sampling: a new approach for efficient reliability estimation for complex systems

Michael D. Shields, V. S. Sundar, V. Sundar, 2015 .

Coarse graining atomistic simulations of plastically deforming amorphous solids.

Chris H. Rycroft, Michael D. Shields, Michael L. Falk, 2017, Physical review. E.

REFINED LATINIZED STRATIFIED SAMPLING: A ROBUST SEQUENTIAL SAMPLE SIZE EXTENSION METHODOLOGY FOR HIGH-DIMENSIONAL LATIN HYPERCUBE AND STRATIFIED DESIGNS

Michael D. Shields, M. Shields, 2016 .

Estimation of evolutionary spectra for simulation of non-stationary and non-Gaussian stochastic processes

George Deodatis, Michael D. Shields, G. Deodatis, 2013 .

Modeling strongly non-Gaussian non-stationary stochastic processes using the Iterative Translation Approximation Method and Karhunen-Loève expansion

Michael D. Shields, M. Shields, Hwanpyo Kim, 2015 .

3rd-order Spectral Representation Method: Part I -- Multi-dimensional random fields with fast Fourier transform implementation

Michael D. Shields, Lohit Vandanapu, Lohit Vandanapu, 2019, 1910.06420.

A simple and efficient methodology to approximate a general non-Gaussian stationary stochastic vector process by a translation process with applications in wind velocity simulation

George Deodatis, Michael D. Shields, G. Deodatis, 2013 .

The effect of prior probabilities on quantification and propagation of imprecise probabilities resulting from small datasets

Michael D. Shields, Jiaxin Zhang, Jiaxin Zhang, 2017, Computer Methods in Applied Mechanics and Engineering.

Simulation of strongly non-Gaussian stochastic vector processes using translation process theory

George Deodatis, Michael D. Shields, G. Deodatis, 2011 .