326 citations · 337 across the 4 of their papers we have counts for
6 papers
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…
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…
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…
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…