NU 2021-247
Inventors
Wei Chen, Daniel Apley, Suraj Yerramilli, Ramin Baghgar Bostanabad (University of California, Irvine)
Short Description
LVGP-PyTorch is a cross-platform Python implementation of the Latent Variable Gaussian Process (LVGP) methodology for creating machine learning models with either qualitative or quantitative inputs or mixture of both. The code provides prediction as well as uncertainty quantification that can be easily integrated with other adaptive learning methods, such as Bayesian Optimization (BO) for decision making or design optimization. The library is a cross-platform library and works on Windows, Mac, and Linux. There are no specific hardware requirements.
Purchase Options
Following purchase code will be delivered through FTP transfer. Further instructions regarding the receipt of code will be sent within one to two weeks. Please ensure that the correct contact email is provided during checkout.
Price: $27,500.00 USD
Citations
Zhang, Y., Tao, S., Chen, W., and Apley, D., “A Latent Variable Approach to Gaussian Process Modeling with Qualitative and Quantitative Factors”, Technometrics, DOI: 10.1080/00401706.2019.1638834, 2019.
Zhang, Y., Apley, D., and Chen, W., “Bayesian Optimization for Materials Design with Mixed Quantitative and Qualitative Variables”, Scientific Report, 10, Article number: 4924, 2020.
For technical questions, please contact:
Professor Wei Chen: weichen@northwestern.edu
Professor Daniel Apley: apley@northwestern.edu
Please note that software code is provided "as is" and all uses must comply with the grant, obligations and limitations included in the click license agreement embedded within the checkout process. All click license terms must be accepted to compete this purchase.