Consider a chemical reactor like a PFR. In the usual, forward problem, taught in CRE courses, you are given the input flow rate and reactant concentration, along with kinetic reaction-rate information, and asked to predict the output concentration or conversion. But suppose, instead, that the conversion is measured experimentally and you are asked to use a model to estimate the input flow rate or input concentration. This is an inverse problem. Such problems arise when using specialized research reactors to determine chemical kinetics information. In this project you will learn and apply machine learning (ML) approaches to such inverse problems. You will start with a model or test problem and apply different ML methods to determine the best one for our application. Then, this approach will be applied to the reactor problem, which is currently being studied in my group.