Nature

Gaussian process regression with physics-guided pseudo-sample augmentation for wear prediction under sparse measurements in milling

Tool wear prediction is essential to ensure machining quality and sustainability. Hybrid physics-data Gaussian process regression (GPR) methods integrate domain knowledge with data-driven learning, ...
Risk

Gaussian process regression for derivative portfolio modeling and application to credit valuation adjustment computations

Modeling counterparty risk is computationally challenging because it requires the simultaneous evaluation of all trades between each counterparty under both market and credit risk. We present a ...
Nature

Hybrid Gaussian process regression with temporal feature extraction for partially interpretable remaining useful life interval prediction in Aeroengine prognostics

The estimation of Remaining Useful Life (RUL) plays a pivotal role in intelligent manufacturing systems and Industry 4.0 technologies. While recent advancements have improved RUL prediction, many ...
AZOM

The Development of Honigs Regression

insights from industryDr. David Honigs, Ph.D.Field Applications ScientistPerkinElmer, Inc. In this interview, Dr. David Honigs, Ph.D, the Field Applications Scientist ...