9 citations · 16 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…
Data-Centric Engineering: integrating simulation, machine learning and statistics. Challenges and Opportunities
Indranil Pan, Lachlan Mason, Omar Matar
Recent advances in machine learning, coupled with low-cost computation, availability of cheap streaming sensors, data storage and cloud technologies, has led to widespread multi-di…
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…
Numerical simulation, clustering and prediction of multi-component polymer precipitation
Pavan Inguva, Lachlan Mason, Indranil Pan +2
Multi-component polymer systems are of interest in organic photovoltaic and drug delivery applications, among others where diverse morphologies influence performance. An improved u…
Data-driven surrogate modelling and benchmarking for process equipment
Gabriel F. N. Gonçalves, Assen Batchvarov, Yuyi Liu +4
In chemical process engineering, surrogate models of complex systems are often necessary for tasks of domain exploration, sensitivity analysis of the design parameters, and optimiz…