A Python library for Probabilistic Design space exploration and OPTimisation

Contemporary engineering design is characterised by products and systems with increasing  complexity coupled with tighter requirements and tolerances. This leads to high epistemic uncertainty due to numerous possible configurations and a high number of design parameters.  Set-Based Design is a methodology capable of handling these design problems, by exploring  and evaluating as many alternatives as possible, before committing to a specific solution.  The Python package PDOPT aims to provide this capability without the high computational  cost associated with the factorial-based design of experiments methods. Additionally, PDOPT  performs the requirement mapping without explicit rule definition. Instead, it utilizes a  probabilistic machine learning model to identify the areas of the design space most promising  for user-provided requirements. This yields a plethora of feasible design points, assisting  designers in understanding the system behaviour and selecting the desired configurations for  further development.