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Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

Computer-aided civil and infrastructure engineering, 2018-06, Vol.33 (6), p.443-458

2018

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IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

IEEE access, 2021, Vol.9, p.112966-112977

2021

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Mesh-based GNN surrogates for time-independent PDEs

Scientific reports, 2024-02, Vol.14 (1), p.3394-3394, Article 3394

2024

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A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources

IEEE access, 2021, Vol.9, p.78128-78137

2021

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Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates

IEEE access, 2020, Vol.8, p.73536-73546

2020

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Survival analysis at multiple scales for the modeling of track geometry deterioration

Proceedings of the Institution of Mechanical Engineers. Part F, Journal of rail and rapid transit, 2018-03, Vol.232 (3), p.842-850

2018

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PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

Computer methods in applied mechanics and engineering, 2023-01, Vol.403, p.115664, Article 115664

2023

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Physics-informed discretization-independent deep compositional operator network

Computer methods in applied mechanics and engineering, 2024-11, Vol.431, p.117274, Article 117274

2024

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End-to-end heterogeneous graph neural networks for traffic assignment

Transportation research. Part C, Emerging technologies, 2024-08, Vol.165, p.104695, Article 104695

2024

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A deep learning solution approach for high-dimensional random differential equations

Probabilistic engineering mechanics, 2019-07, Vol.57, p.14-25

2019

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A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions

Journal of computational physics, 2018-10, Vol.371, p.137-151

2018

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Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions

Computer methods in applied mechanics and engineering, 2017-05, Vol.318, p.937-956

2017

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A preconditioning approach for improved estimation of sparse polynomial chaos expansions

Computer methods in applied mechanics and engineering, 2018-12, Vol.342, p.474-489, Article 474

2018

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Efficient Collection of Connected Vehicles Data With Precision Guarantees

IEEE transactions on intelligent transportation systems, 2020-11, Vol.21 (11), p.4637-4645

2020

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Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data

Reliability engineering & system safety, 2018-01, Vol.169, p.422-436

2018

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Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Nuclear engineering and design, 2018-08, Vol.335, p.339-355

2018

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Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests

Transportation research record, 2019-05, Vol.2673 (5), p.564-573

2019

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Neural network surrogate models for aerodynamic analysis in truck platoons: Implications on autonomous freight delivery

International Journal of Transportation Science and Technology, 2024-02

2024

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Efficient training of physics‐informed neural networks via importance sampling

Computer-aided civil and infrastructure engineering, 2021-08, Vol.36 (8), p.962-977

2021

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Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE

Nuclear engineering and design, 2018-08, Vol.335, p.417-431

2018

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Inverse unsaturated-zone flow modeling for groundwater recharge estimation: a regional spatial nonstationary approach

Hydrogeology journal, 2022-08, Vol.30 (5), p.1529-1549

2022

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Gradient based design optimization under uncertainty via stochastic expansion methods

Computer methods in applied mechanics and engineering, 2016-07, Vol.306, p.47-76

2016

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Multiscale Markov models with random transitions for energy demand management

Energy and buildings, 2013-06, Vol.61, p.267-274

2013

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Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model

Nuclear engineering and design, 2017-08, Vol.319, p.185-200

2017

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Random Markov decision processes for sustainable infrastructure systems

Structure and infrastructure engineering, 2015-05, Vol.11 (5), p.655-667

2015

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Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks

IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data

Mesh-based GNN surrogates for time-independent PDEs

A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources

Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates

Survival analysis at multiple scales for the modeling of track geometry deterioration

PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations

Physics-informed discretization-independent deep compositional operator network

End-to-end heterogeneous graph neural networks for traffic assignment

A deep learning solution approach for high-dimensional random differential equations

A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions

Divide and conquer: An incremental sparsity promoting compressive sampling approach for polynomial chaos expansions

A preconditioning approach for improved estimation of sparse polynomial chaos expansions

Efficient Collection of Connected Vehicles Data With Precision Guarantees

Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian process, Part 1: Theory

Predicting Near-Term Train Schedule Performance and Delay Using Bi-Level Random Forests

Neural network surrogate models for aerodynamic analysis in truck platoons: Implications on autonomous freight delivery

Efficient training of physics‐informed neural networks via importance sampling

Inverse uncertainty quantification using the modular Bayesian approach based on Gaussian Process, Part 2: Application to TRACE

Inverse unsaturated-zone flow modeling for groundwater recharge estimation: a regional spatial nonstationary approach

Gradient based design optimization under uncertainty via stochastic expansion methods

Multiscale Markov models with random transitions for energy demand management

Inverse uncertainty quantification of TRACE physical model parameters using sparse gird stochastic collocation surrogate model

Random Markov decision processes for sustainable infrastructure systems

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