64 citations · 206 across the 13 of their papers we have counts for
13 papers
Optimal sensor placement for reconstructing wind pressure field around buildings using compressed sensing
Xihaier Luo, Ahsan Kareem, Shinjae Yoo
Deciding how to optimally deploy sensors in a large, complex, and spatially extended structure is critical to ensure that the surface pressure field is accurately captured for subs…
A Deep Finite Difference Emulator for the Fast Simulation of Coupled Viscous Burgers' Equation
Xihaier Luo, Yihui Ren, Wei Xu +3
This work proposes a deep learning-based emulator for the efficient computation of the coupled viscous Burgers' equation with random initial conditions. In a departure from traditi…
A Machine Learning-based Characterization Framework for Parametric Representation of Nonlinear Sloshing
Xihaier Luo, Ahsan Kareem, Liting Yu +1
The growing interest in creating a parametric representation of liquid sloshing inside a container stems from its practical applications in modern engineering systems. The resonant…
Machine learning based automated identification of thunderstorms from anemometric records using shapelet transform
Monica Arul, Ahsan Kareem
Detection of thunderstorms is important to the wind hazard community to better understand extreme winds field characteristics and associated wind induced load effects on structures…
Data Anomaly Detection for Structural Health Monitoring of Bridges using Shapelet Transform
Monica Arul, Ahsan Kareem
With the wider availability of sensor technology, a number of Structural Health Monitoring (SHM) systems are deployed to monitor civil infrastructure. The continuous monitoring pro…
Applications of shapelet transform to time series classification of earthquake, wind and wave data
Monica Arul, Ahsan Kareem
Autonomous detection of desired events from large databases using time series classification is becoming increasingly important in civil engineering as a result of continued long-t…
Probabilistic Evolution of Stochastic Dynamical Systems: A Meso-scale Perspective
Chao Yin, Xihaier Luo, Ahsan Kareem
Stochastic dynamical systems arise naturally across nearly all areas of science and engineering. Typically, a dynamical system model is based on some prior knowledge about the unde…
Shapelets for earthquake detection
Monica Arul, Ahsan Kareem
This paper introduces EQShapelets (EarthQuake Shapelets) a time-series shape-based approach embedded in machine learning to autonomously detect earthquakes. It promises to overcome…
Deep convolutional neural networks for uncertainty propagation in random fields
Xihaier Luo, Ahsan Kareem
The development of a reliable and robust surrogate model is often constrained by the dimensionality of the problem. For a system with high-dimensional inputs/outputs (I/O), convent…
Bayesian deep learning with hierarchical prior: Predictions from limited and noisy data
Xihaier Luo, Ahsan Kareem
Datasets in engineering applications are often limited and contaminated, mainly due to unavoidable measurement noise and signal distortion. Thus, using conventional data-driven app…
On the accuracy of stiff-accurate diagonal implicit Runge-Kutta methods for finite volume based Navier-Stokes equations
Jiawei Wan, Ahsan Kareem, Haili Liao +1
The paper aims at developing low-storage implicit Runge-Kutta methods which are easy to implement and achieve higher-order of convergence for both the velocity and pressure in the…
Dynamics of random pressure fields over bluff bodies: a dynamic mode decomposition perspective
Xihaier Luo, Ahsan Kareem
Aerodynamic pressure field over bluff bodies immersed in boundary layer flows is correlated both in space and time. Conventional approaches for the analysis of distributed aerodyna…
Lessons Learned from an Experiment in Crowdsourcing Complex Citizen Engineering Tasks with Amazon Mechanical Turk
Matthew Staffelbach, Peter Sempolinski, David Hachen +5
We investigate the feasibility of obtaining highly trustworthy results using crowdsourcing on complex engineering tasks. Crowdsourcing is increasingly seen as a potentially powerfu…