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ADO TRIM Operator

ado-trim is an operator plugin for the Accelerated Discovery Orchestrator (ADO), providing the Transfer Refined Iterative Modeling (TRIM) characterization operator.

TRIM is designed to efficiently build a surrogate model of a complex system. It is ideal for scenarios where exploring a parameter space is time-consuming or expensive. TRIM intelligently samples just enough points to create a stable and accurate predictive model, saving significant time and resources.

How it Works

The TRIM operator works in two main phases:

  1. No-Priors Characterization: If the system has not been measured before, TRIM starts by sampling a small, representative set of points using a space-filling algorithm to get a baseline understanding of the parameter space.

  2. Iterative Modeling: This phase begins by using all currently available data to train a single preliminary surrogate model. The feature importance from this model is used to order for all remaining unmeasured points. TRIM then enters a loop where it:

    • Samples the next point and adds it to the dataset.
    • Trains a model on the gathered data.
    • Evaluates the expected improvement of a model trained on a larger dataset by comparing the new model's performance against that of previous models.

This loop continues until the improvement is below a threshold, at which point TRIM automatically stops. Finally, it trains one high-quality model on all collected data and saves it for your use. It also outputs a file containing the measured values and predictions for all points in your space.

Installation

You can install the TRIM operator and its dependencies (including ado-core) directly from PyPI:

pip install ado-trim

More Information

To learn more about TRIM and explore the full capabilities of ADO, including detailed documentation, configuration guides, and additional examples, visit the official ADO website:

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