Metamodeling is becoming a rather popular means to approximate the expensive
simulations in today's complex engineering design problems since accurate metamodels …

Kernel methods are among the most popular techniques in machine learning. From a
regularization perspective they play a central role in regularization theory as they provide a …

This paper presents a big data analytics method for the proactive mitigation of grounding
risk. The model encompasses the dynamics of ship motion trajectories while accounting for …

This work leverages recent advances in probabilistic machine learning to discover
governing equations expressed by parametric linear operators. Such equations involve, but …

Multi-output regression problems have extensively arisen in modern engineering
community. This article investigates the state-of-the-art multi-output Gaussian processes …

For more than two centuries, solutions of differential equations have been obtained either
analytically or numerically based on typically well-behaved forcing and boundary conditions …

This work brings together two powerful concepts in Gaussian processes: the variational
approach to sparse approximation and the spectral representation of Gaussian processes …

Digital twin technology has significant promise, relevance and potential of widespread
applicability in various industrial sectors such as aerospace, infrastructure and automotive …

Recently there has been an increasing interest in regression methods that deal with multiple
outputs. This has been motivated partly by frameworks like multitask learning, multisensor …

Bayesian optimization (BO) is a powerful approach for seeking the global optimum of
expensive black-box functions and has proven successful for fine tuning hyper-parameters …