We discuss the applicability of an open-source generative design for the automated design of computational experiments with structures in physical environments for various scientific fields. It may be used for scientific experiments where the searched structure can be represented as a set of 2D non-oriented graphs with any topology (grids, polygons, trees), and the physical environment can be described with any numerical model (classic or data-driven). The proposed framework gives the tools to efficiently explore a space of experiment configurations with generative AI models and evolutionary algorithms. The results are shown in examples from different fields: design of microfluidic devices, coastal engineering, research on heat transfer, and acoustics. Due to the framework's focus on working with structures as graphs, it is possible to pre-train generative NN that is used to create an initial population of optimized structures. The framework finds application in diverse areas such as coastal engineering, acoustics, engineering design, heat transfer, hydrodynamics, and medicine.