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2020 – today
- 2024
[i18]Weiheng Zhong, Hadi Meidani:
Physics-informed Mesh-independent Deep Compositional Operator Network. CoRR abs/2404.13646 (2024)- [i17]Weiheng Zhong, Hadi Meidani:
Physics-Informed Geometry-Aware Neural Operator. CoRR abs/2408.01600 (2024) - [i16]Tong Liu, Hadi Meidani:
Heterogeneous Graph Sequence Neural Networks for Dynamic Traffic Assignment. CoRR abs/2408.04131 (2024) - 2023
- [c3]Tong Liu
, Hadi Meidani:
Optimizing Seismic Retrofit of Bridges: Integrating Efficient Graph Neural Network Surrogates and Transportation Equity. CPS-IoT Week Workshops 2023: 367-372 - [c2]Weiheng Zhong, Tanwi Mallick, Jane MacFarlane, Hadi Meidani, Prasanna Balaprakash:
Graph Pyramid Autoformer for Long- Term Traffic Forecasting. ICMLA 2023: 384-391 - [i15]Rini Jasmine Gladstone, Helia Rahmani, Vishvas Suryakumar, Hadi Meidani, Marta D'Elia, Ahmad Zareei:
GNN-based physics solver for time-independent PDEs. CoRR abs/2303.15681 (2023) - [i14]Weiheng Zhong, Hadi Meidani, Jane MacFarlane:
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction. CoRR abs/2305.00985 (2023) - [i13]Tong Liu, Hadi Meidani:
Heterogeneous Graph Neural Networks for Data-driven Traffic Assignment. CoRR abs/2310.13193 (2023) - 2022
- [i12]Weiheng Zhong, Hadi Meidani:
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations. CoRR abs/2203.11363 (2022) - [i11]Weiheng Zhong, Tanwi Mallick, Hadi Meidani, Jane MacFarlane, Prasanna Balaprakash:
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting. CoRR abs/2209.13123 (2022) - [i10]Tong Liu, Hadi Meidani:
Graph Neural Network Surrogate for seismic reliability analysis of highway bridge system. CoRR abs/2210.06404 (2022) - [i9]Rini Jasmine Gladstone, Mohammad Amin Nabian, Hadi Meidani:
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks. CoRR abs/2210.14320 (2022) - [i8]Amir Kazemi, Hadi Meidani:
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram. CoRR abs/2211.02620 (2022) - 2021
- [j9]Negin Alemazkoor, Hadi Meidani:
A Data-Driven Multi-Fidelity Approach for Traffic State Estimation Using Data From Multiple Sources. IEEE Access 9: 78128-78137 (2021) - [j8]Amir Kazemi, Hadi Meidani:
IGANI: Iterative Generative Adversarial Networks for Imputation With Application to Traffic Data. IEEE Access 9: 112966-112977 (2021) - [j7]Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani:
Efficient training of physics-informed neural networks via importance sampling. Comput. Aided Civ. Infrastructure Eng. 36(8): 962-977 (2021) - [i7]Mohammad Amin Nabian, Rini Jasmine Gladstone, Hadi Meidani:
Efficient training of physics-informed neural networks via importance sampling. CoRR abs/2104.12325 (2021) - [i6]Rini Jasmine Gladstone, Mohammad Amin Nabian, Vahid Keshavarzzadeh, Hadi Meidani:
Robust Topology Optimization Using Variational Autoencoders. CoRR abs/2107.10661 (2021) - 2020
- [j6]Negin Alemazkoor, Hadi Meidani:
Fast Probabilistic Voltage Control for Distribution Networks With Distributed Generation Using Polynomial Surrogates. IEEE Access 8: 73536-73546 (2020) - [j5]Mohammad Amin Nabian, Hadi Meidani:
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis. J. Comput. Inf. Sci. Eng. 20(1) (2020) - [j4]Negin Alemazkoor, Hadi Meidani:
Efficient Collection of Connected Vehicles Data With Precision Guarantees. IEEE Trans. Intell. Transp. Syst. 21(11): 4637-4645 (2020) - [i5]Mohammad Amin Nabian, Hadi Meidani:
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo. CoRR abs/2008.01604 (2020) - [i4]Amir Kazemi, Hadi Meidani:
IGANI: Iterative Generative Adversarial Networks for Imputation Applied to Prediction of Traffic Data. CoRR abs/2008.04847 (2020)
2010 – 2019
- 2018
- [j3]Mohammad Amin Nabian, Hadi Meidani:
Deep Learning for Accelerated Seismic Reliability Analysis of Transportation Networks. Comput. Aided Civ. Infrastructure Eng. 33(6): 443-458 (2018) - [j2]Negin Alemazkoor, Hadi Meidani:
A near-optimal sampling strategy for sparse recovery of polynomial chaos expansions. J. Comput. Phys. 371: 137-151 (2018) - [j1]Xu Wu, Tomasz Kozlowski, Hadi Meidani:
Kriging-based inverse uncertainty quantification of nuclear fuel performance code BISON fission gas release model using time series measurement data. Reliab. Eng. Syst. Saf. 169: 422-436 (2018) - [c1]Negin Alemazkoor, Shiyu Wang, Hadi Meidani:
A Recursive Data-driven Model for Traffic Flow Predictions for Locations with Faulty Sensors. ITSC 2018: 1646-1651 - [i3]Mohammad Amin Nabian, Hadi Meidani:
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations. CoRR abs/1806.02957 (2018) - [i2]Mohammad Amin Nabian, Hadi Meidani:
Physics-Informed Regularization of Deep Neural Networks. CoRR abs/1810.05547 (2018) - 2017
- [i1]Mohammad Amin Nabian, Hadi Meidani:
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks. CoRR abs/1708.08551 (2017)
Coauthor Index
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