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| Publication | Type | Year |
|---|---|---|
Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional DataWccm-apcom 2022
|
Conference Presentation – 2022 Conference Presentation | 2022 |
Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQMachine Learning and Deep Learning Conference
|
Conference Presentation – 2022 Conference Presentation | 2022 |
A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate ModelsSIAM Conference on Uncertainty Quantification
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Conference Presentation – 2022 Conference Presentation | 2022 |
Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent InversionSIAM Conference on Uncertainty Quantification
|
Conference Poster – 2022 Conference Poster | 2022 |
Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data15th World Congress on Computational Mechanics
|
Abstract – 2021 Abstract | 2021 |
Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-Consistent InversionSiam Uq 2022
|
Abstract – 2021 Abstract | 2021 |
| Document Title | Type | Year |