Physical Knowledge on Historical Data Matters More Than Enforcing Physical Constraints on the Forecast
ScientificML
This paper proposes PIRNN to enhance interpretability and accuracy of time series forecasting for physical processes by incorporating physical knowledge from historical data during training.
See LICENSE file Physics-Informed Neural NetworksTime Series ForecastingRecurrent Neural Networks