The notebook mentioned in the previous log post illustrates a basic comparison of grid search vs. random search vs. Bayesian optimization. One of SDL-Demo's appeals is the ability to illustrate more advanced optimization topics such as multi-objective optimization, constrained optimization, multi-fidelity optimization, high-dimensional optimization, and combinations thereof.

So far, I've made example notebooks for multi-objective and continuous multi-fidelity optimization, with more to come. Feedback welcome!

## Multi-objective:

In this notebook, we will use multi-objective optimization to find optimal trade-offs between each of the 8 recorded wavelengths. This is in contrast to minimizing a scalarized objective such as MAE, RMSE, or Frechet distance relative to a target spectrum. ...

## Multi-fidelity (Introduction):

In the previous notebook, we covered multi-objective optimization: i.e. looking at optimal tradeoffs between multiple, sometimes competing, objectives. Here, we'll take a look a multi-fidelity optimization. First, let's start off by loosely defining a fidelity parameter as a parameter that controls the quality of the information being obtained. ...

## Continuous multi-fidelity:

In the previous notebook, I provided a brief introduction of multi-fidelity optimization in the context of the physical sciences. This notebook will cover Bayesian optimization using two continuous fidelity parameters (atime and astep). We'll compare the total integration time using the multi-fidelity optimization with the integration time costs of running the simulation ...

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