Recursive Cokriging Surrogate Model Development

I need assistance in developing a recursive cokriging multi-fidelity surrogate model primarily for optimization purposes. The model will utilize simulated data as its primary data source. I am currently using Python to generate this simulated data. The method we would like to implement is based on Loic le gratiet and Garnier 2014 paper, see links below. Old R code is available from the original authors. We would like to use the method to approximate a high-fidelity reservoir simulator with a low-fidelity reservoir simulator. [login to view URL] [login to view URL],2f7b99cc281f2702,[login to view URL] Key Requirements: - Develop a recursive cokriging model for optimization. - Utilize simulated data as the data source. - Ensure compatibility with Python-generated data. Ideal Skills and Experience: - Proficiency in Gaussian processes and developing surrogate models. - Experience with optimization techniques. - Strong background in handling simulated data, coding and statistics - Expertise in Python for data generation and model integration. - Expertise in R.

Project ID: 39720516