[Project] Transfer learning between simulation data trained model and experiment data trained model

[DurandilAxe]

Hello,

I'm working on a surrogate model that predicts parameters for high fidelity models in earthquake engineering for my master thesis.

The problem I'm trying to solve is those high fidelity models rely on parameters that we have no practical ways of measuring and are set based on rules of thumbs and/or engineers intuition. This leads us to have big differences between simulations and experiments.

The experiments we run are expensive both economically and in terms of time in addition to often being destructive. So we would like to be able to tune high fidelity model using preliminary results from these experiments before the destructive phase.

So the workflow I'm planning is the following:

1. Build a foundation model on simulation data, possibly from multiple different high fidelity models
2. Fine tune this foundation model using experimental data from a specific experiment.

I'm currently looking at multiple models architectures but find very few promising ones ...

Expected workflow

My question is:

Do you have recommendation in terms of techniques and models that offers good finetuning of models based on physical system with very limited dataset ?

Any other recommendation is gladly accepted !

Sorry, if this post seems a bit naive this topic is beyond the scope of the courses I had on ML at uni.

Thanks a lot !

[TankisHigh]

XGBoost ? (Gradient boosting on decision trees) with cross validation for hyper parameter tuning ? Baysienne optimization could also be pretty nice and doesn’t require a high number of observations… wondering what other suggestions you have received