Because it’s important to share what you have understood. These are my insights developed from reading the work of experts as I continue to explore. I will also include links to the original sources for further reading.
Understanding chaos: the sensitivity to intitial conditions and the butterfly effect.
https://dataspace.copernicus.eu/news/2026-9-25-new-openeo-community-example-generate-terramind-embeddings-sentinel-1-and-sentinel-2
Hypothesis Is a neural operator well trained? Is it sensitive to the boundary conditions or forcing conditions as the physical model? Feature interactions In many classification tasks, there are not trivial interactions between features, such that the importance of one feature much depends on the presence of another. References https://arxiv.org/abs/2609.36216 https://arxiv.org/abs/2601.23072 Butterfly effect in neural operators is missing. The sensitivity of the neural operator to small changes in the input is not well understood. This can lead to unexpected behavior and poor generalization. https://arxiv.org/pdf/2608.25835 ...
Resources to consult for further reading and understanding of the topic of Uncertainty Quantification in SCIML https://iris.sissa.it/handle/20.500.11767/153272 - this gives you an idea of the current methods, metrics, and the type of systems to use for testing and benchmarking.