csjiezhao / Physics-Based-Deep-Learning
☆23Updated 2 years ago
Alternatives and similar repositories for Physics-Based-Deep-Learning:
Users that are interested in Physics-Based-Deep-Learning are comparing it to the libraries listed below
- Physics Informed Deep Learning for Traffic State Estimation: Illustrations with LWR and CTM Models☆15Updated last year
- Physics-informed Dyna-style model-based deep reinforcement learning for dynamic control☆49Updated 2 years ago
- ☆30Updated 5 years ago
- Papers on Spatial-Temporal Graph Neural Networks☆26Updated 2 years ago
- Official implementation of "Physics-Informed Long-Sequence Forecasting From Multi-Resolution Spatiotemporal Data".☆10Updated 2 years ago
- Deep identification of symbolic open-form PDEs via enhanced reinforcement-learning☆32Updated 2 months ago
- Discovering dynamic patterns from spatiotemporal data with time-varying low-rank autoregression. (IEEE TKDE'24)☆19Updated 2 months ago
- Deep Learning - Predicting using Neural Ordinary Differential Equations - torchdiffeq.☆15Updated 4 years ago
- Physics Informed Deep Learning - Traffic State Estimation☆18Updated last year
- Pytorch implementation of Spatoi-temporal Differential Equation Network (STDEN).☆37Updated 2 years ago
- Differentiable Physics-informed Graph Networks☆65Updated 5 years ago
- Source code for "Deep Variational Koopman Models: Inferring Koopman Observations for Uncertainty-Aware Dynamics Modeling and Control" fro…☆37Updated 5 years ago
- Official PyTorch implementation of "Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics" (https://openreview.net/forum…☆50Updated 5 years ago
- Empower Traffic Simulation via Foundation Model☆23Updated last year
- ☆116Updated last year
- ☆38Updated 2 years ago
- ☆14Updated 2 years ago
- ☆13Updated 3 years ago
- Spatiotemporal graph convolutional network for wind speed prediction.☆36Updated 3 years ago
- Transportation data online prediction☆48Updated 3 years ago
- Consistent Koopman Autoencoders☆71Updated last year
- Graph Neural Networks utilization for Spatiotemporal graphs. These methods will be applied into the problem of forecasting traffic flow o…☆18Updated 3 years ago
- Comparison for time series prediction using Neural-ODEs and Neural-Flows☆12Updated 2 years ago
- A python implementation of Physics-informed Spline Learning for nonlinear dynamics discovery.☆25Updated 3 years ago
- Research project conducted at Pacific Northwest National Laboratory, exploring the use of physics-informed autoencoders to predict fluid …☆34Updated 2 years ago
- Implementation of STG2Seq: Spatial-temporal Graph to Sequence Model for Multi-step Passenger Demand Forecasting☆32Updated 4 years ago
- An official 're'-implementation of Physics-induced graph neural network: An application to wind-farm power estimation (PGNN).☆28Updated 3 years ago
- ☆13Updated 2 years ago
- ☆22Updated last year
- ☆42Updated 3 years ago