Prediction of combustion-related properties of (oxygenated) hydrocarbons is an important and challenging task for which quantitative structure-property relationship (QSPR) models are frequently employed.
The combination of data-driven modelling with mechanistic model components, reduces the data demand and enables extrapolation of data-driven models.
With this package, you can generate mixed-integer linear programming (MIP) models of trained artificial neural networks (ANNs) using the rectified linear unit (ReLU) activation function. At the moment, only TensorFlow sequential models are supported. Interfaces to either the Pyomo or Gurobi modeling environments are offered.
This tool contains functionality for the conversion between PFD-graphs/P&ID-graphs and SFILES 2.0 strings. In the paper, we describe the structure of the graphs, notation rules of the SFILES 2.0, and the conversion algorithm.
Machine Learning models for Optimization (MeLOn) is toolbox that integrates machine-learning models into optimization problems.