326 citations · 1k across the 19 of their papers we have counts for
19 papers
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…
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram
Amir Kazemi, Hadi Meidani
A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case. The framework aims at capturing th…
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…
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
Weiheng Zhong, Hadi Meidani
We propose a new class of physics-informed neural networks, called physics-informed Variational Autoencoder (PI-VAE), to solve stochastic differential equations (SDEs) or inverse p…
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…
IGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Data
Amir Kazemi, Hadi Meidani
Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence o…
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…
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…
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks
Mohammad Amin Nabian, Hadi Meidani
Natural disasters can have catastrophic impacts on the functionality of infrastructure systems and cause severe physical and socio-economic losses. Given budget constraints, it is…
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…