Case ID: M23-251P^

Published: 2024-05-13 13:46:40

Last Updated: 1715608000


Mohit Malu
Giulia Pedrielli
Gautam Dasarathy
Andreas Spanias

Technology categories

Computing & Information TechnologyPhysical Science

Technology keywords

Machine Learning
PS-Computing and Information Technology

Licensing Contacts

Physical Sciences Team

Gaussian Process Modeling for Heterogeneous Functions

Many modern science and engineering applications, such as machine learning, hyperparameter optimization of neural networks, robotics, cyber-physical systems, etc., call for modeling techniques to model black-box functions.  Gaussian Process (GP) modeling is a popular Bayesian non-parametric framework heavily employed to model expensive black-box functions for analysis such as prediction or optimization. 

Traditionally, GP models assume stationarity of the underlying, unknown function.  As a result, a unique covariance kernel (with constant hyperparameters) can be used over the entire domain.  However, many real-world systems such as cyber-physical systems and controls often require modeling and optimization of functions that are locally stationary and globally non-stationary across the domain.  Thus, with many current GP modeling frameworks operating under assumptions of stationarity of an underlying, unknown function, there is a need for a modeling technique that makes no such assumptions.

Researchers at Arizona State University have developed a modeling technique for black-box functions.  In particular, a Gaussian Process (GP) modeling technique for unknown, globally non-stationary heterogeneous functions that can be applied in various science and engineering applications.

Related publication: Class GP: Gaussian Process Modeling for Heterogeneous Functions

Potential Applications:

  • Black-box modeling for science and engineering applications:
    • Machine learning
    • Neural networks
    • Robotics
    • Cyber-physical systems

Benefits and Advantages:

  • Modeling and optimization of unknown, globally non-stationary heterogeneous functions
  • Applicable for black-box modeling for many modern-day science and engineering applications