Page Not Found
Page not found. Your pixels are in another canvas.
Sitemap
A list of all the posts and pages found on the site. For you robots out there is an XML version available for digesting as well.
Pages
Page not in menu
This is a page not in th emain menu
Functionally graded materials beams subjected to bilateral constraints: Structural instability and material topology
Published in International Journal of Mechanical Sciences, 2021
Download here
Postbuckling of multi-direction anisotropic constrained functionally graded material beams
Published in Behavior and Mechanics of Multifunctional Materials, 2021
Download here
Bridging finite element and deep learning: High-resolution stress distribution prediction in structural components
Published in Frontiers of Structural and Civil Engineering, 2022
Download here
Deep learning paradigm for prediction of stress distribution in damaged structural components with stress concentrations
Published in Advances in Engineering Software, 2022
Download here
Neuro-DynaStress: Predicting Dynamic Stress Distributions in Structural Components
Published in arXiv preprint, 2022
Download here
NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems
Published in European Conference on Computer Vision (ECCV), 2022
Download here
Methods for the rapid detection of boundary condition variations in structural systems
Published in Smart Materials, Adaptive Structures and Intelligent Systems (SMASIS), 2022
Download here
Physics informed neural network for dynamic stress prediction
Published in Applied Intelligence, 2023
Download here
Estimating field parameters from multiphysics governing equations with scarce data
Published in ICLR Workshop on AI4DifferentialEquations In Science, 2024
Download here
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
Published in Nature Communications, 2024
Download here
ParaFIND: Parameter Field Inference on Non-uniform Domains using Neural Network.
Published in NeurIPS Workshop on Data-driven and Differentiable Simulations, Surrogates, and Solvers. 2024
Download here
Posts
AI-driven Automated Structural Health Monitoring (see full blog here)
Mechanics-Informed Autoencoder Enables Automated Detection and Localization of Unforeseen Structural Damage
See blog post at Nature Research Communities Infrastructure resilience is emerging as a crucial need in the face of increased risks and hazards impacts. This highlights the need for reliable, cost-effective, and scalable infrastructure condition assessment and monitoring approaches. The need is driven by recent real-world failures that have led to catastrophic consequences, from the unnoticed cracks in the I-40 Bridge in Memphis, which resulted in an emergency closure, to the collapse of a bridge in Pittsburgh in 2022 due to undetected corrosion. These incidents reveal the limitations of traditional inspection methods and emphasize the need for more intelligent and efficient solutions. To prevent future catastrophes, it is essential to develop accurate baseline models that are scalable, generalizable, and tailored to the unique characteristics of each structure. AI-driven automated Structural Health Monitoring (SHM) systems offer a promising avenue to address the shortcomings of existing manual inspections and sensor technologies. By leveraging advanced algorithms and machine learning techniques, these systems can enhance early detection and localization of structural damage, thereby significantly improving infrastructure resilience and safety.
portfolio
Poster 1: Mechanics-informed autoencoder enables automated detection and localization of unforeseen structural damage
Our work is pubulished at Nature Communications and selected as Editors’ Highlights! Please check out the Paper and GitHub repository. 
Poster 2: NeuralSI: Structural Parameter Identification in Nonlinear Dynamical Systems
Check out our publication here. 
Poster 3: Estimating field parameters from multiphysics governing equations with scarce data
A portion of this work is available at here. 
Poster 4: Data-driven mechanical behavior modeling of granular biomass materials
Check out our publication here. 
publications
teaching
CE-221 Statics
Teaching Assitant, Michigan State University, Civil and Environmental Department, 2020-2024
CE-461: Computational Methods in Engineering
Teaching Assitant, Michigan State University, Civil and Environmental Department, 2020, 2022, 2024
IST-597: Physics Informed Machine Learning
Delivering Selected Lectures as a Postdoc, Pennsylvania State University, Information Sciences and Technology, Fall 2024