Numerical simulations of high energy-density experiments require equation of state (EOS)
models that relate a material's thermodynamic state variables—specifically pressure …

The typical way to conduct data-driven prognosis is to train a degradation model with
historical data, then apply the model to predict failure for in-service units. Most existing works …

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 …

Gaussian processes (GPs) and neural networks (NNs) are two kinds of function
approximation models that are widely used for health prognostics. As an emerging meta …

Sensor technology has become increasingly prevalent in various domains of human life.
However, the collected data often contains missing values to varying degrees. Moreover …

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 …

The rapid development of sensor technologies allows the acquisition of high dimensional
sensing data. Multi-output modeling techniques have been developed to leverage the data …

Image-based monitoring techniques have achieved great success in many engineering
applications. However, most existing image monitoring methods require fully observed …

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 …