326 citations · 1k across the 19 of their papers we have counts for
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Efficient Collection of Connected Vehicles Data with Precision Guarantees
Negin Alemazkoor, Hadi Meidani
Connected vehicles disseminate detailed data, including their position and speed, at a very high frequency. Such data can be used for accurate real-time analysis, prediction and co…
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
Mohammad Amin Nabian, Hadi Meidani
In this paper, we introduce a physics-driven regularization method for training of deep neural networks (DNNs) for use in engineering design and analysis problems. In particular, w…
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
Mohammad Amin Nabian, Hadi Meidani
Developing efficient numerical algorithms for the solution of high dimensional random Partial Differential Equations (PDEs) has been a challenging task due to the well-known curse…
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Xu Wu, Tomasz Kozlowski, Hadi Meidani +1
Inverse Uncertainty Quantification (UQ) is a process to quantify the uncertainties in random input parameters while achieving consistency between code simulations and physical obse…
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory
Xu Wu, Tomasz Kozlowski, Hadi Meidani +1
In nuclear reactor system design and safety analysis, the Best Estimate plus Uncertainty (BEPU) methodology requires that computer model output uncertainties must be quantified in…