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05/01/2023
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Traffic forecasting is an important issue in intelligent traffic systems...
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03/28/2023
GNN-based physics solver for time-independent PDEs
Physics-based deep learning frameworks have shown to be effective in acc...
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10/25/2022
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
We present FO-PINNs, physics-informed neural networks that are trained u...
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10/12/2022
Graph Neural Network Surrogate for seismic reliability analysis of highway bridge system
Rapid reliability assessment of transportation networks can enhance prep...
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09/27/2022
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Accurate traffic forecasting is vital to an intelligent transportation s...
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03/21/2022
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
We propose a new class of physics-informed neural networks, called physi...
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07/19/2021
Robust Topology Optimization Using Variational Autoencoders
Topology Optimization is the process of finding the optimal arrangement ...
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04/26/2021
Efficient training of physics-informed neural networks via importance sampling
Physics-Informed Neural Networks (PINNs) are a class of deep neural netw...
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08/11/2020
IGANI: Iterative Generative Adversarial Networks for Imputation Applied to Prediction of Traffic Data
Generative adversarial networks (GANs) are implicit generative models th...
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08/03/2020
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo
In this paper, we propose the Adaptive Physics-Informed Neural Networks ...
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10/31/2018
Efficient Collection of Connected Vehicles Data with Precision Guarantees
Connected vehicles disseminate detailed data, including their position a...
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10/11/2018
Physics-Informed Regularization of Deep Neural Networks
This paper presents a novel physics-informed regularization method for t...
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06/08/2018
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
Developing efficient numerical algorithms for high dimensional random Pa...
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01/28/2018
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 2: Application to TRACE
Inverse Uncertainty Quantification (UQ) is a process to quantify the unc...
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01/05/2018
Inverse Uncertainty Quantification using the Modular Bayesian Approach based on Gaussian Process, Part 1: Theory
In nuclear reactor system design and safety analysis, the Best Estimate ...
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09/22/2017
A preconditioning approach for improved estimation of sparse polynomial chaos expansions
Compressive sampling has been widely used for sparse polynomial chaos (P...
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08/28/2017
