A critical bottleneck for scientific progress is the costly nature of computer simulations for
complex systems. Surrogate models provide an appealing solution: such models are trained …

Building a predictive model that rapidly adapts to real-time condition monitoring (CM) time-
series data is critical for engineering systems/units. Unfortunately, many current methods …

Profile monitoring is a widely used tool in quality control. The rapid development of sensor
technology has created unprecedented opportunities for multi-channel profile data …

Multioutput Gaussian process (MGP) is commonly used as a transfer learning method to
leverage information among multiple outputs. A key advantage of MGP is providing …

Chemical contents, the important quality indicators are crucial for the modeling of sintering
process. However, the lack of these data can result in the biasedness of state estimation in …

This paper explores a federated learning approach that automatically selects the number of
latent processes in multi-output Gaussian processes (MGPs). The MGP has seen great …

Multi-output Gaussian process (MGP) is commonly used as a transfer learning method to
leverage information among multiple outputs. A key advantage of MGP is providing …

Building a predictive model that rapidly adapts to real-time condition monitoring (CM)
signals is critical for engineering systems/units. Unfortunately, many current methods suffer …

Data-driven engine surrogate models have been widely used to emulate in-cylinder trends
of pressure and heat release rate for a wide variety of applications. For example, engines …