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Data-based Discovery of Governing Equations
Without a prior definition of the model structure, first a free-form of the equation is discovered, and then calibrated and validated against the available data.
Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems
The region of interest can be specified based on the localization features of the solution, user interest, and correlation length of the random material properties.
Advances in Bayesian Probabilistic Modeling for Industrial Applications
The methodology, called GE's Bayesian Hybrid Modeling (GEBHM), is a probabilistic modeling method, based on the Kennedy and O'Hagan framework, that has been continuously scaled-up and industrialized over several years.
Data-driven discovery of free-form governing differential equations
We present a method of discovering governing differential equations from data without the need to specify a priori the terms to appear in the equation.
A Strategy for Adaptive Sampling of Multi-fidelity Gaussian Process to Reduce Predictive Uncertainty
Multi-fidelity Gaussian process is a common approach to address the extensive computationally demanding algorithms such as optimization, calibration and uncertainty quantification.
