Generating and using data has become an inseparable part of our everyday lives. Emergence of “smart” technologies has also added yet bigger sources/sinks for data, with most of the data being generated and consumed by machines. This has changed the data landscape dramatically, putting forth a challenge to handle (and make sense of) large datasets originating from various sources i.e. sensors, models, software, or a combination of these. One of the most convenient methods to deal with this challenge is the application of Machine Learning (ML) methods via introduction of surrogate models and classification.
Our Model Development Suite (MoDS) software offers a range of surrogate model generation techniques. For example, High Dimensional Model Representation (HDMR) technique is applied here for generating a surrogate model as a function of a high-dimensional (~80-100) input variable space, for a renewable algae-derived fuel production plant. HDMR algorithm provides the associated global sensitivities for indicators such as the greenhouse gas (GHG) footprint and energy balance ratios (EBR).
In another example, Deep Kernel Learning (DKL) that is a composition of a deep neural network with a Gaussian Process (GP) is applied to train and validate a surrogate model for vehicular emissions.
MoDS also offers the ability for Hyper-Parameter Optimisation (HPO), i.e. for auto-tuning the parameters intrinsic to the DKL technique. Once generated, these data-driven models could be used as stand-alone “executable” models through MoDS API in a range of third-party software. Read about MoDS toolkit here.
Contact us to learn more about CMCL’s offerings for data-driven models.