36 citations · 91 across the 9 of their papers we have counts for
11 papers
Kriging and Gaussian processes: interconnected frameworks for spatial modelling and prediction
Hossein Mohammadi
Spatial modelling and inference arise across many areas of science and engineering, with Kriging and Gaussian process (GP) regression serving as two widely used predictive models.…
An ensemble-based approach for multi-fidelity emulation and adaptive sampling
Hossein Mohammadi
High-resolution simulation models are essential for representing complex physical systems, yet their substantial computational cost severely limits the number of feasible high-fide…
Cross-Validation Based Adaptive Sampling for Multi-Level Gaussian Process Models
Louise Kimpton, James Salter, Tim Dodwell +2
Complex computer codes or models can often be run in a hierarchy of different levels of complexity ranging from the very basic to the sophisticated. The top levels in this hierarch…
Variance-based global sensitivity analysis of numerical models using R
Hossein Mohammadi, Peter Challenor, Clémentine Prieur
Sensitivity analysis plays an important role in the development of computer models/simulators through identifying the contribution of each (uncertain) input factor to the model out…
Sequential adaptive design for emulating costly computer codes
Hossein Mohammadi, Peter Challenor
Gaussian processes (GPs) are generally regarded as the gold standard surrogate model for emulating computationally expensive computer-based simulators. However, the problem of trai…
Emulating complex dynamical simulators with random Fourier features
Hossein Mohammadi, Peter Challenor, Marc Goodfellow
A Gaussian process (GP)-based methodology is proposed to emulate complex dynamical computer models (or simulators). The method relies on emulating the numerical flow map of the sys…
Cross-validation based adaptive sampling for Gaussian process models
Hossein Mohammadi, Peter Challenor, Daniel Williamson +1
In many real-world applications, we are interested in approximating black-box, costly functions as accurately as possible with the smallest number of function evaluations. A comple…
Emulating computer models with step-discontinuous outputs using Gaussian processes
Hossein Mohammadi, Peter Challenor, Marc Goodfellow +1
In many real-world applications we are interested in approximating costly functions that are analytically unknown, e.g. complex computer codes. An emulator provides a fast approxim…
Emulating dynamic non-linear simulators using Gaussian processes
Hossein Mohammadi, Peter Challenor, Marc Goodfellow
The dynamic emulation of non-linear deterministic computer codes where the output is a time series, possibly multivariate, is examined. Such computer models simulate the evolution…
Small ensembles of kriging models for optimization
Hossein Mohammadi, Rodolphe Le Riche, Eric Touboul
The Efficient Global Optimization (EGO) algorithm uses a conditional Gaus-sian Process (GP) to approximate an objective function known at a finite number of observation points and…
An analytic comparison of regularization methods for Gaussian Processes
Hossein Mohammadi, Rodolphe Le Riche, Nicolas Durrande +2
Gaussian Processes (GPs) are a popular approach to predict the output of a parameterized experiment. They have many applications in the field of Computer Experiments, in particular…