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08/27/2023
On Active Learning for Gaussian Process-based Global Sensitivity Analysis
This paper explores the application of active learning strategies to ada...
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04/15/2023
Learning in latent spaces improves the predictive accuracy of deep neural operators
Operator regression provides a powerful means of constructing discretiza...
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01/31/2023
Active Learning-based Domain Adaptive Localized Polynomial Chaos Expansion
The paper presents a novel methodology to build surrogate models of comp...
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12/07/2022
General multi-fidelity surrogate models: Framework and active learning strategies for efficient rare event simulation
Estimating the probability of failure for complex real-world systems usi...
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11/20/2022
Multifidelity Active Learning for Failure Estimation of TRISO Nuclear Fuel
The Tristructural isotropic (TRISO)-coated particle fuel is a robust nuc...
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09/19/2022
Physics-Informed Machine Learning of Dynamical Systems for Efficient Bayesian Inference
Although the no-u-turn sampler (NUTS) is a widely adopted method for per...
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08/12/2022
Bayesian Inference with Latent Hamiltonian Neural Networks
When sampling for Bayesian inference, one popular approach is to use Ham...
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04/20/2022
Deep transfer learning for partial differential equations under conditional shift with DeepONet
Traditional machine learning algorithms are designed to learn in isolati...
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03/09/2022
On the influence of over-parameterization in manifold based surrogates and deep neural operators
Constructing accurate and generalizable approximators for complex physic...
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02/09/2022
A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problems
Constructing surrogate models for uncertainty quantification (UQ) on com...
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01/06/2022
Reliability Estimation of an Advanced Nuclear Fuel using Coupled Active Learning, Multifidelity Modeling, and Subset Simulation
Tristructural isotropic (TRISO)-coated particle fuel is a robust nuclear...
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10/29/2021
Data-driven Uncertainty Quantification in Computational Human Head Models
Computational models of the human head are promising tools for estimatin...
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09/28/2021
Grassmannian diffusion maps based surrogate modeling via geometric harmonics
In this paper, a novel surrogate model based on the Grassmannian diffusi...
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09/28/2021
Simulation of non-stationary and non-Gaussian random processes by 3rd-order Spectral Representation Method: Theory and POD implementation
This paper introduces the 3^rd-order Spectral Representation Method for ...
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07/21/2021
Manifold learning-based polynomial chaos expansions for high-dimensional surrogate models
In this work we introduce a manifold learning-based method for uncertain...
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11/23/2020
Probabilistic modeling of discrete structural response with application to composite plate penetration models
Discrete response of structures is often a key probabilistic quantity of...
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03/24/2020
Data-driven surrogates for high dimensional models using Gaussian process regression on the Grassmann manifold
This paper introduces a surrogate modeling scheme based on Grassmannian ...
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11/22/2019
3rd-order Spectral Representation Method: Part II – Ergodic Multi-variate random processes with fast Fourier transform
The second in a two-part series, this paper extends the 3rd-order Spectr...
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10/14/2019
3rd-order Spectral Representation Method: Part I – Multi-dimensional random fields with fast Fourier transform implementation
This paper introduces a generalised 3rd-order Spectral Representation Me...
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03/24/2018
Probability measure changes in Monte Carlo simulation
The objective of Bayesian inference is often to infer, from data, a prob...
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10/09/2017