Generalizing beyond the training domain remains a critical challenge in the adoption of machine learning (ML) for modeling the physical world. While explicit physical models offer guarantees and can be applied in any valid environment, establishing causal relationships between model variables, statistical models learn correlations solely from samples, limiting their applicability to the training domain context. In this presentation, we will discuss the main challenges posed by this issue when modeling spatio-temporal phenomena and highlight recent advancements aimed at resolving it.