24 citations · 66 across the 6 of their papers we have counts for
6 papers
G-PARC: Graph-Physics Aware Recurrent Convolutional Neural Networks for Spatiotemporal Dynamics on Unstructured Meshes
Jack T. Beerman, Tyler J. Abele, Mehdi Taghizadeh +6
Physics-aware recurrent convolutional networks (PARC) have demonstrated strong performance in predicting nonlinear spatiotemporal dynamics by embedding differential operators direc…
Multi-fidelity Machine Learning for Uncertainty Quantification and Optimization
Ruda Zhang, Negin Alemazkoor
In system analysis and design optimization, multiple computational models are typically available to represent a given physical system. These models can be broadly classified as hi…
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…
A preconditioning approach for improved estimation of sparse polynomial chaos expansions
Negin Alemazkoor, Hadi Meidani
Compressive sampling has been widely used for sparse polynomial chaos (PC) approximation of stochastic functions. The recovery accuracy of compressive sampling highly depends on th…
A Near-Optimal Sampling Strategy for Sparse Recovery of Polynomial Chaos Expansions
Negin Alemazkoor, Hadi Meidani
Compressive sampling has become a widely used approach to construct polynomial chaos surrogates when the number of available simulation samples is limited. Originally, these expens…
Divide and Conquer: An Incremental Sparsity Promoting Compressive Sampling Approach for Polynomial Chaos Expansions
Negin Alemazkoor, Hadi Meidani
This paper introduces an efficient sparse recovery approach for Polynomial Chaos (PC) expansions, which promotes the sparsity by breaking the dimensionality of the problem. The pro…