Inverse prediction of design parameters for forming soft morphing structures using deep generative modelsBrzin, Tomaž (Avtor) Brojan, Miha (Mentor) Jawed, M. Khalid (Komentor) inverse designdesign parametersgenerative adversarial networksmorphing structureskirigami compositesexperimentsIn this doctoral dissertation, we present a data-driven approach for the inverse design of morphing soft kirigami composites that utilize the principles of kirigami and strain mismatch to achieve various target shapes. At the center of our methodology is the generative adversarial network, a neural network framework designed to train the generative model tasked to generate the necessary design parameters. By using a pre-trained simulator network, we condition the generative model to generate not only feasible but also accurate design parameters that are used to produce composites that morph into the target shapes. Our findings demonstrate that the generative model effectively predicts the required design parameters, enabling the realization of complex target shapes from planar designs, specifically the composite structures that are able to self-deploy into various 3D shapes; and composite beams that can demonstrate complex motions between the prescribed target positions. We verify our results with the ones found in the literature, by performing numerical simulations and conducting accurate desktop experiments – we have fabricated the composites according to the generated design parameters and found excellent agreement between the target and fabricated shapes. We also compare the method to the competing approaches and demonstrate its superiority.[T. Brzin]20252025-07-09 07:30:05Doktorsko delo/naloga170537sl