When I have similar doubt, I ask myself; "forget ML, will statistics help you? Will just defining some rules will help you?" People in that industry already have some kind of idea about how to predict, learn their rules. By the way, I am not suggesting that you should not use ML. I am just asking you to look from a different angle.

Expert knowledge could be encoded as rules whose output is used as features for a machine learning system. These rules would accept data you already have, and produce new data as conclusions which would be fed as extra variables to a modeling algorithm.

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You might try Snorkel. The gist is that domain experts write rules and those rules are fed into ML. If that company doesn't work, I'm pretty sure there are alternatives. Or maybe they had their work in a Python library... it's been a while.

Compared to traditional ML, the benefit is that you're involving the subject matter experts more and giving them a say more directly. That tends to ensure that they're bought in to the approach. Having been in healthcare ML for a while, getting buy-in can be very challenging.

Look into hybrid modeling, there are multiple ways to do this

Hi! There is relatively new paradigm, 'Physics informed Machine Learning"

Here is a nice review of the different techniques:

https://www.nature.com/articles/s42254-021-00314-5

The most popular approach is based on physics regularization on neural networks.

PS: link for the paper is offered by the autor:

https://www.researchgate.net/publication/351814752_Physics-informed_machine_learning

Im looking into something similar, essentially combining data driven ML with a knowledge base, but in the context of explainable AI and predictive maintenance.

I have stumbled across something called "Logic Tensor Networks" (search for the paper) which might help in your situation. I need to look into it more, but it combines ML + knowledge bases + fuzzy logic.

Hope you find a solution!

Or you could use ml to predict the error of the rule based system or simulation.

Are you trying to do research, or solve a problem? Building expert systems out of neural networks is still a new, experimental idea. If you just want to get the job done you may want to pick more proven methods.

If you use synthetic data (from the crop simulation models), the model will kind of reverse-engineer it (it will learn what the simulation models are doing).

Using a mix of it with real word data, is like regularizing your model (adding a prior) to the simulation rules.

This is something that makes sense, and mixing data often is used. But "making sense" doesn't necessarily means it helps ... that depends a lot on your application. Also the next question is how much synthetic data you may want to mix ... fundamentally you'll have to figure it out by trial&error and having some way of measuring if things are getting better for whatever your extrinsic goal is (your business objective).

Have any paper in mind that speaks about this stuff ?

No, but the idea is pretty straightforward. Assuming that experts can provide domain knowledge that can be coded as conditions or rules (IF engine_temperature > 95 AND coolant_pressure < 12 THEN engine_status = "CRITICAL"), these can be used to generate 0/1 flags based on existing data to augment the training variables.

This can be made much more complex by using actual expert systems or fuzzy logic. There are entire sections of the technical library for those. For fuzzy logic, I would recommend:

"The Fuzzy Systems Handbook"

by Earl Cox

ISBN-13: 978-0121942700

Thanks, yes that is true, the recent days I had a look into Bayesian Statistics. That might be an alternative to pure ML that I am considering right now

I would say both. I have an actual problem (to predict crop yield as accurate as possible) but the way there is definitely a research problem... What proven methods would you think about?

Thanks for that suggestion! Yeah I had thoughts about this. The problem is that plant crop probably has not so binary solutions like a engine status... Maybe a very simple "rule" (e.g. a functions of water access and crop yield) could be added into the loss function. If this easy expert knowledge output a high probability that the plant died (and yield=0) all y_train could be set to 0 also.... However, crop growth relies on so many events that happens during growth, that it would mean to implement many many rules...

Thanks a lot! That looks like a very interesting approach! I will have a detailed look into it!

Hi, interesting! Do expert models exist for your problem already or would it be only the knowledge database you want to combine?

Yes thats true. This is also what I thought about. Using a mixed dataset or transfer learning approaches (first train on synthetic data, then retrain on real world) should incorporate the domain knowledge. But you are right, right know thats just an hypothesis...but I will test it!

There might be general existing models that i can fit to my problem, but i havent looked into it yet.

Im interested regardless if they already exist and if i can combine them with other data sources.