32 citations · 71 across the 11 of their papers we have counts for
4 papers · 1 filter
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Panagiotis Tsilifis, Piyush Pandita, Sayan Ghosh +3
We present a Bayesian approach to identify optimal transformations that map model input points to low dimensional latent variables. The "projection" mapping consists of an orthonor…
Advances in Bayesian Probabilistic Modeling for Industrial Applications
Sayan Ghosh, Piyush Pandita, Steven Atkinson +5
Industrial applications frequently pose a notorious challenge for state-of-the-art methods in the contexts of optimization, designing experiments and modeling unknown physical resp…
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Sayan Ghosh, Jesper Kristensen, Yiming Zhang +2
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification.…
Towards Scalable Gaussian Process Modeling
Piyush Pandita, Jesper Kristensen, Liping Wang
Numerous engineering problems of interest to the industry are often characterized by expensive black-box objective experiments or computer simulations. Obtaining insight into the p…