DECISION MAKING IN SCIENTIFIC MACHINE LEARNING

Scientific Machine Learning is built on the science-of-counting, is deductively solvable, and well-suited to business and human applications that naturally count. From the Gibbs formalism, Scientific Machine Learning produces unique and exact scientific measurements that define the state of the time-series. Timeseries itself defines a geometric structure tailor made for prediction, optimization and decision making. Inventory management decisions will demonstrate Scientific Machine Learning without introducing models or model bias.