Harness for Adaptive Learning

HAL is an adaptive design and learning tool for efficient sampling and meta-modeling of “black-box” systems/models.

Features and

An HAL user interface.


  • HAL’s space-filling experimental designs are more efficient than full factorial or fine-grid designs for high-dimensional models
  • A variety of machine learning techniques are used and compared to determine best fitting regression/classification meta-models
  • Validation and verification are built into the process to ensure full understanding and accurate meta-model representation of the harnessed model
  • Data visualization and exploration allow the user to identify and visualize patterns, anomalies, and trends across observed data and meta-models


  • Alleviates the analytic burden of developing experimental designs to explore the sample space (modeling and simulation autopilot for data generation)
  • Assesses multiple inter-dependent models to derive their interactions
  • Enables rapid exploration of complex models to quickly find areas of interest applicable to a specific question/study
An HAL user interface.