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Found 4 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.
Attention-based Spatial-Temporal Graph Neural ODE for Traffic Prediction
Traffic forecasting is an important issue in intelligent traffic systems (ITS).
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.
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