326 citations · 1k across the 19 of their papers we have counts for
9 papers · 1 filter
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Weiheng Zhong, Hadi Meidani, Jane Macfarlane
Traffic forecasting is an important issue in intelligent traffic systems (ITS). Graph neural networks (GNNs) are effective deep learning models to capture the complex spatio-tempor…
GNN-based physics solver for time-independent PDEs
Rini Jasmine Gladstone, Helia Rahmani, Vishvas Suryakumar +3
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inpu…
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
Tong Liu, Hadi Meidani
Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems. Network reliability…
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Weiheng Zhong, Tanwi Mallick, Hadi Meidani +2
Accurate traffic forecasting is vital to an intelligent transportation system. Although many deep learning models have achieved state-of-art performance for short-term traffic fore…
Robust Topology Optimization Using Multi-Fidelity Variational Autoencoders
Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh +1
Robust topology optimization (RTO), as a class of topology optimization problems, identifies a design with the best average performance while reducing the response sensitivity to i…
Efficient training of physics-informed neural networks via importance sampling
Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani
Physics-Informed Neural Networks (PINNs) are a class of deep neural networks that are trained, using automatic differentiation, to compute the response of systems governed by parti…
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo
Mohammad Amin Nabian, Hadi Meidani
In this paper, we propose the Adaptive Physics-Informed Neural Networks (APINNs) for accurate and efficient simulation-free Bayesian parameter estimation via Markov-Chain Monte Car…
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…