Machine Learning in Hydroresearch
Is emulation just an application of machine learning to an specific problem?
The most frequent answer is probably: yes! However, there is more to emulation than just model-free statistical learning methods.
Emulation make its biggest contributions, specially in terms of reduced samples (it is so expensive to run CFD simulations!) when mechanistic models can be combined with the learning algorithms. What most people are unaware of is that most machine learning methods do not play well with models. We put part of this discussion on our IAHR extended abstract1. We will publish a full version in the Water special issue including the role of models in emulation.
One of EmuMore’s biggest concerns is to find methods (or develop them) that can exploit mechanistic knowledge. We are currently looking at causal models.
Carbajal, J.P. & Bellos, V. (2018), An overview of the role of Machine Learning in hydraulic and hydrological modeling. Proc. 5th IAHR European Congress. doi:10.31224/osf.io/wgm72 ↩