This paper presents a novel spatial discretisation method for the reliab...
Learning the kernel parameters for Gaussian processes is often the
compu...
Learning precise surrogate models of complex computer simulations and
ph...
This paper is concerned with adaptive mesh refinement strategies for the...
Despite recent advances in automated machine learning, model selection i...
Multi-output regression problems are commonly encountered in science and...
This paper studies bulk-surface splitting methods of first order for
(se...
We investigate active learning in Gaussian Process state-space models
(G...
We consider constrained partial differential equations of hyperbolic typ...
This paper is devoted to the construction of exponential integrators of ...
Ordinary differential equations (ODE) are widely used for modeling in Sy...