Implementing Weather Prediction Using Physics Informed Neural Networks (PINNS)

Physics-based neural networks (pinn’s) have proven to be a promising methodology for combining domain knowledge and data control learning, particularly in modeling complex dynamic systems. This study presents a hybrid deep learning framework that integrates physics-based limitations for predicting climate variables and Bidirectional Long Short-Term Memory (BILSTM). The aim is to predict atmospheric conditions near the creation, particularly temperature and geopolitical heights, using continuous observations from the created data. The BILSTM model is trained to simultaneously record the underlying time patterns of data and is in compliance with the physics of atmospheric processes. The concept of physical loss is introduced. This is derived from the simplified thermal diffusion equation to punish violations of the basic energy diffusion properties. This loss of physics, combined with the standard loss of standard square error (MSE), ensures that the model’s predictions are not only accurate but physically consistent. Comparative reviews show that physical models improve predictionstability and achieve greater compliance with physical principles compared to purely data-driven baselines. Furthermore, the addition of physics-based regularization improves generalization through invisible samples, which helps reduce overadaptation, especially in border regions where traditional models often fail. By embedding physical knowledge directly into the training process, this model provides a way to reliable and interpretive weather and climate prediction systems that have a more comprehensive effect on promoting scientific machine learning in modeling the Earth system. IndexTerms: Physics-Informed Neural Networks (PINNs), Bidirectional Long Short-Term Memory (BiLSTM), Climate Forecasting, Geopotential Height, Temperature Prediction, Diffusion Equation, Physics-Based Loss, Time Series Forecasting.