42 citations · 43 across the 5 of their papers we have counts for
5 papers
Rule-based Evolutionary Bayesian Learning
Themistoklis Botsas, Lachlan R. Mason, Omar K. Matar +1
In our previous work, we introduced the rule-based Bayesian Regression, a methodology that leverages two concepts: (i) Bayesian inference, for the general framework and uncertainty…
Multiphase flow applications of non-intrusive reduced-order models with Gaussian process emulation
Themistoklis Botsas, Indranil Pan, Lachlan R. Mason +1
Reduced-order models (ROMs) are computationally inexpensive simplifications of high-fidelity complex ones. Such models can be found in computational fluid dynamics where they can b…
A Bayesian approach to deconvolution in well test analysis
Themistoklis Botsas, Jonathan A. Cumming, Ian H. Jermyn
In petroleum well test analysis, deconvolution is used to obtain information about the reservoir system. This information is contained in the response function, which can be estima…
Rule-based Bayesian regression
Themistoklis Botsas, Lachlan R. Mason, Indranil Pan
We introduce a novel rule-based approach for handling regression problems. The new methodology carries elements from two frameworks: (i) it provides information about the uncertain…
Latent-space time evolution of non-intrusive reduced-order models using Gaussian process emulation
Romit Maulik, Themistoklis Botsas, Nesar Ramachandra +2
Non-intrusive reduced-order models (ROMs) have recently generated considerable interest for constructing computationally efficient counterparts of nonlinear dynamical systems emerg…