Publications

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Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data

Wccm-apcom 2022

Tian Yu NMN Yen, Timothy Michael Wildey

Conference Presentation – 2022 Conference Presentation 2022

Embedded uncertainty estimation for data-driven surrogates to enable trustworthy ML for UQ

Machine Learning and Deep Learning Conference

Timothy Michael Wildey, Gianluca Geraci, Michael S. Eldred, John Davis Jakeman, Owen Davis, Teresa Portone, Tian Yu NMN Yen, Bryan William Reuter, Alex Gorodetsky, Ahmad Rushdi, Daniele Schiavazzi, Lauren Partin

Conference Presentation – 2022 Conference Presentation 2022

A Probabilistic Characterization of Aleatoric and Epistemic Uncertainty in Solutions to Stochastic Inverse Problems Using Machine Learning Surrogate Models

SIAM Conference on Uncertainty Quantification

Timothy Michael Wildey, Troy Butler, Tian Yu NMN Yen

Conference Presentation – 2022 Conference Presentation 2022

Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-consistent Inversion

SIAM Conference on Uncertainty Quantification

Tian Yu NMN Yen, Timothy Michael Wildey, Troy Butler

Conference Poster – 2022 Conference Poster 2022

Using Manifold Learning to Enable Computationally Efficient Stochastic Inversion with High-dimensional Data

15th World Congress on Computational Mechanics

Tian Yu NMN Yen, Timothy Michael Wildey

Abstract – 2021 Abstract 2021

Quantifying Aleatoric and Epistemic Uncertainties in RLC Circuits with Data-Consistent Inversion

Siam Uq 2022

Tian Yu NMN Yen

Abstract – 2021 Abstract 2021
Document Title Type Year