Physics-induced Graph Neural Network (PGNN)

An official 're'-implementation of Physics-induced graph neural network: An application to wind-farm power estimation ( PGNN).

Presentation file

wind_farm_presentation.pdf summarizes the results of the journal paper.

Notice

We lost the asset for regenerating the journal version of PGNN and also the lost asset is considered to be obsolete due to the updates of dependencies. Here, we provide re-implementation of PGNN based on DGL. The original implementation were based on the older version of pytorch and floris. However, since we submitted the journal paper, considerable changes has been made on the dependencies, especially floris.

We provide slightly different hyperparameter choices of PGNN so that the new implementation shows similar (or better) predictive performances as compared to the journal version.

Difference of re-implementation from the journal version.

• Use Euclidean coordinates of wind turbines as additional node features.
• Use BatchNorm in front of the edge_model,node_model and global_model of the PGN layers. We experimentally confirmed that using BatchNorm greatly improves the training speed and also slightly improves the asymptotic performance of PGNNs when augmenting Euclidean coordinates as inputs.

Dependencies

• pytorch (1.7.1)
• dgl (0.5.x)
• GraphFloris (0.20) - For data generation
• Adamp - An optimizer
• python-box - For handling experiment configurations
• Ray - For parallel training data generation
• wandb - For logging

Physics-induced Graph Network (PGN) layer

class PGN(nn.Module):
    """
    Pytorch-DGL implementation of the physics-induced Graph Network Layer
    "https://www.sciencedirect.com/science/article/pii/S0360544219315555"
    """

    def __init__(self,
                 edge_model: nn.Module,
                 node_model: nn.Module,
                 global_model: nn.Module,
                 residual: bool,
                 use_attention: bool,
                 edge_aggregator: str = 'mean',
                 global_node_aggr: str = 'mean',
                 global_edge_aggr: str = 'mean'):

edge_model, node_model and global_model are any differentiable functions that computes the updated edge, node, and global embedding respectively. In the examples, we used multi-layer perceptron as the edge_model, node_model and global_model.

Physics-induced Attention

class PhysicsInducedAttention(nn.Module):

    def __init__(self,
                 input_dim=3,
                 use_approx=True,
                 degree=5):

PhysicsInducedAttention computes the attention score based on the steady-state wake deficit factor proposed by J. Park et al.

Quick start

Training new PGNN model from scratch

Using wandb logger and ray-based parallel training data generation

python train_pgnn.py -device cuda:0 (or 'cpu')

or simply

python train_pgnn_naive.py -device cuda:0 (or 'cpu')

Test trained model

We also provide pre-trained PGNN models. The hyperparameter and trained parameters of PGNN are saved in config.yaml and model.pt respectively. The following code loads the pre-trained PGNN from disk and evaluates the model.

python eval_model.py