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Michael D. Shields
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2020 – today
- 2024
- [j21]Lukás Novák, Himanshu Sharma, Michael D. Shields
:
Physics-informed polynomial chaos expansions. J. Comput. Phys. 506: 112926 (2024) - [j20]Dimitris G. Giovanis
Polynomial chaos expansions on principal geodesic Grassmannian submanifolds for surrogate modeling and uncertainty quantification. J. Comput. Phys. 519: 113443 (2024) - [j19]Mohit Chauhan
On active learning for Gaussian process-based global sensitivity analysis. Reliab. Eng. Syst. Saf. 245: 109945 (2024) - [j18]Lohit Vandanapu, Michael D. Shields
Simulation of non-Gaussian wind field as a 3rd-order stochastic wave. Reliab. Eng. Syst. Saf. 245: 109960 (2024) - [i21]Denny Thaler, Somayajulu L. N. Dhulipala, Franz Bamer, Bernd Markert, Michael D. Shields:
Reliability Analysis of Complex Systems using Subset Simulations with Hamiltonian Neural Networks. CoRR abs/2401.05244 (2024) - [i20]Dimitris G. Giovanis, Dimitrios Loukrezis, Ioannis G. Kevrekidis, Michael D. Shields:
Polynomial Chaos Expansions on Principal Geodesic Grassmannian Submanifolds for Surrogate Modeling and Uncertainty Quantification. CoRR abs/2401.16683 (2024) - [i19]Himanshu Sharma, Lukás Novák, Michael D. Shields:
Physics-constrained polynomial chaos expansion for scientific machine learning and uncertainty quantification. CoRR abs/2402.15115 (2024) - [i18]George D. Pasparakis, Lori Graham-Brady, Michael D. Shields:
Bayesian neural networks for predicting uncertainty in full-field material response. CoRR abs/2406.14838 (2024) - [i17]Bahador Bahmani, Somdatta Goswami, Ioannis G. Kevrekidis, Michael D. Shields:
A Resolution Independent Neural Operator. CoRR abs/2407.13010 (2024) - [i16]Varun Kumar, Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis:
Synergistic Learning with Multi-Task DeepONet for Efficient PDE Problem Solving. CoRR abs/2408.02198 (2024) - 2023
- [j17]Katiana Kontolati, Somdatta Goswami
On the influence of over-parameterization in manifold based surrogates and deep neural operators. J. Comput. Phys. 479: 112008 (2023) - [j16]Somayajulu L. N. Dhulipala
Efficient Bayesian inference with latent Hamiltonian neural networks in No-U-Turn Sampling. J. Comput. Phys. 492: 112425 (2023) - [j15]Dimitrios Tsapetis, Michael D. Shields
UQpy v4.1: Uncertainty quantification with Python. SoftwareX 24: 101561 (2023) - [i15]Lukás Novák, Michael D. Shields, Václav Sadílek, Miroslav Vorechovský
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion. CoRR abs/2301.13635 (2023) - [i14]Katiana Kontolati, Somdatta Goswami, George Em Karniadakis, Michael D. Shields:
Learning in latent spaces improves the predictive accuracy of deep neural operators. CoRR abs/2304.07599 (2023) - [i13]Dimitrios Tsapetis, Michael D. Shields, Dimitris G. Giovanis, Audrey Olivier, Lukás Novák, Promit Chakroborty, Himanshu Sharma, Mohit Chauhan, Katiana Kontolati, Lohit Vandanapu, Dimitrios Loukrezis, Michael Gardner:
UQpy v4.1: Uncertainty Quantification with Python. CoRR abs/2305.09572 (2023) - [i12]Mohit Chauhan, Mariel Ojeda-Tuz, Ryan Catarelli
On Active Learning for Gaussian Process-based Global Sensitivity Analysis. CoRR abs/2308.14220 (2023) - [i11]Lukás Novák, Himanshu Sharma, Michael D. Shields:
Physics-Informed Polynomial Chaos Expansions. CoRR abs/2309.01697 (2023) - 2022
- [j14]Katiana Kontolati, Dimitrios Loukrezis
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. J. Comput. Phys. 464: 111313 (2022) - [j13]Somayajulu L. N. Dhulipala
Active learning with multifidelity modeling for efficient rare event simulation. J. Comput. Phys. 468: 111506 (2022) - [j12]Somdatta Goswami
Deep transfer operator learning for partial differential equations under conditional shift. Nat. Mac. Intell. 4(12): 1155-1164 (2022) - [j11]Somayajulu L. N. Dhulipala
Reliability estimation of an advanced nuclear fuel using coupled active learning, multifidelity modeling, and subset simulation. Reliab. Eng. Syst. Saf. 226: 108693 (2022) - [j10]Ketson R. M. dos Santos
Grassmannian Diffusion Maps-Based Dimension Reduction and Classification for High-Dimensional Data. SIAM J. Sci. Comput. 44(2): 250- (2022) - [i10]Katiana Kontolati, Dimitrios Loukrezis, Dimitrios G. Giovanis, Lohit Vandanapu, Michael D. Shields:
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems. CoRR abs/2202.04648 (2022) - [i9]Katiana Kontolati, Somdatta Goswami, Michael D. Shields, George Em Karniadakis:
On the influence of over-parameterization in manifold based surrogates and deep neural operators. CoRR abs/2203.05071 (2022) - [i8]Somdatta Goswami, Katiana Kontolati, Michael D. Shields, George Em Karniadakis:
Deep transfer learning for partial differential equations under conditional shift with DeepONet. CoRR abs/2204.09810 (2022) - [i7]Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Bayesian Inference with Latent Hamiltonian Neural Networks. CoRR abs/2208.06120 (2022) - [i6]Somayajulu L. N. Dhulipala, Yifeng Che, Michael D. Shields
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference. CoRR abs/2209.09349 (2022) - [i5]Promit Chakroborty, Somayajulu L. N. Dhulipala, Yifeng Che, Wen Jiang, Benjamin W. Spencer, Jason D. Hales, Michael D. Shields:
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation. CoRR abs/2212.03375 (2022) - 2021
- [i4]Somayajulu L. N. Dhulipala, Michael D. Shields, Benjamin W. Spencer, Chandrakanth Bolisetti, Andrew E. Slaughter, Vincent M. Laboure, Promit Chakroborty:
Active Learning with Multifidelity Modeling for Efficient Rare Event Simulation. CoRR abs/2106.13790 (2021) - [i3]Katiana Kontolati, Dimitrios Loukrezis, Ketson R. M. dos Santos, Dimitrios G. Giovanis
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models. CoRR abs/2107.09814 (2021) - 2020
- [j9]Jiaxin Zhang
On the quantification and efficient propagation of imprecise probabilities with copula dependence. Int. J. Approx. Reason. 122: 24-46 (2020) - [j8]Audrey Olivier
UQpy: A general purpose Python package and development environment for uncertainty quantification. J. Comput. Sci. 47: 101204 (2020) - [i2]Dimitris G. Giovanis, Michael D. Shields:
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold. CoRR abs/2003.11910 (2020) - [i1]K. R. M. dos Santos, Dimitris G. Giovanis
Grassmannian diffusion maps based dimension reduction and classification for high-dimensional data. CoRR abs/2009.07547 (2020)
2010 – 2019
- 2019
- [j7]Susanna Gallani
Budgeting, Psychological Contracts, and Budgetary Misreporting. Manag. Sci. 65(6): 2924-2945 (2019) - [c1]B. S. Aakash, D. Perez-Roldan, A. Ibrahim, Michael D. Shields
Uncertainty quantification (UQ) as an archetype for research: Integrating UQ into undergraduate research education. FIE 2019: 1-5 - 2018
- [j6]Dimitris G. Giovanis
Uncertainty quantification for complex systems with very high dimensional response using Grassmann manifold variations. J. Comput. Phys. 364: 393-415 (2018) - [j5]Anindya Bhaduri
Stochastic collocation approach with adaptive mesh refinement for parametric uncertainty analysis. J. Comput. Phys. 371: 732-750 (2018) - [j4]Michael D. Shields
Adaptive Monte Carlo analysis for strongly nonlinear stochastic systems. Reliab. Eng. Syst. Saf. 175: 207-224 (2018) - 2016
- [j3]Michael D. Shields
The generalization of Latin hypercube sampling. Reliab. Eng. Syst. Saf. 148: 96-108 (2016) - 2015
- [j2]Michael D. Shields
Refined Stratified Sampling for efficient Monte Carlo based uncertainty quantification. Reliab. Eng. Syst. Saf. 142: 310-325 (2015) - 2014
- [j1]Kirubel Teferra, Michael D. Shields
Mapping model validation metrics to subject matter expert scores for model adequacy assessment. Reliab. Eng. Syst. Saf. 132: 9-19 (2014)
Coauthor Index
aka: Dimitrios G. Giovanis
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last updated on 2024-10-16 20:33 CEST by the dblp team
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