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Found 16 papers, 2 papers with code
Physics-informed Mesh-independent Deep Compositional Operator Network
However, the training of neural operators typically demands large training datasets, the acquisition of which can be prohibitively expensive.
Heterogeneous Graph Neural Networks for End-to-End Traffic Assignment and Traffic Flow Learning
The traffic assignment problem is one of the significant components of traffic flow analysis for which various solution approaches have been proposed.
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Traffic forecasting is an important issue in intelligent traffic systems (ITS).
GNN-based physics solver for time-independent PDEs
Physics-based deep learning frameworks have shown to be effective in accurately modeling the dynamics of complex physical systems with generalization capability across problem inputs.
FO-PINNs: A First-Order formulation for Physics Informed Neural Networks
Physics-Informed Neural Networks (PINNs) are a class of deep learning neural networks that learn the response of a physical system without any simulation data, and only by incorporating the governing partial differential equations (PDEs) in their loss function.
Time Series Synthesis via Multi-scale Patch-based Generation of Wavelet Scalogram
A framework is proposed for the unconditional generation of synthetic time series based on learning from a single sample in low-data regime case.
Graph Neural Network Surrogate for Seismic Reliability Analysis of Highway Bridge Systems
Rapid reliability assessment of transportation networks can enhance preparedness, risk mitigation, and response management procedures related to these systems.
Explainable Graph Pyramid Autoformer for Long-Term Traffic Forecasting
Moreover, most of the existing deep learning traffic forecasting models are black box, presenting additional challenges related to explainability and interpretability.
PI-VAE: Physics-Informed Variational Auto-Encoder for stochastic differential equations
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 problems involving SDEs.
Robust Topology Optimization Using Variational Autoencoders
Robust topology optimization (RTO) also incorporates the effect of input uncertainties and produces a design with the best average performance of the structure while reducing the response sensitivity to input uncertainties.
Efficient training of physics-informed neural networks via importance sampling
This importance sampling approach is straightforward and easy to implement in the existing PINN codes, and also does not introduce any new hyperparameter to calibrate.
IGANI: Iterative Generative Adversarial Networks for Imputation with Application to Traffic Data
Increasing use of sensor data in intelligent transportation systems calls for accurate imputation algorithms that can enable reliable traffic management in the occasional absence of data.
Adaptive Physics-Informed Neural Networks for Markov-Chain Monte Carlo
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 Carlo (MCMC).
Physics-Driven Regularization of Deep Neural Networks for Enhanced Engineering Design and Analysis
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.
A Deep Neural Network Surrogate for High-Dimensional Random Partial Differential Equations
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 of dimensionality.
Deep Learning for Accelerated Reliability Analysis of Infrastructure Networks
This paper presents a deep learning framework for accelerating infrastructure system reliability analysis.
