The field of neuro-fuzzy networks and multiscale optimization is rapidly evolving, with a focus on developing innovative methods for concurrent optimization and data-driven modeling. Recent research has explored the application of gradient-based neuroplastic adaptation for the concurrent optimization of neuro-fuzzy networks, enabling the optimization of parameters and structure simultaneously. This approach has shown promise in settings previously unapproachable for neuro-fuzzy networks, such as online reinforcement learning for vision-based tasks. In the area of multiscale optimization, data-driven approaches have been proposed for the design of spinodoid architected materials and soft functionally graded materials. These approaches utilize neural networks and Gaussian Process surrogates to optimize material distribution and topology, providing clear physical insights into the design process. Noteworthy papers in this area include:

• A study on gradient-based neuroplastic adaptation for concurrent optimization of neuro-fuzzy networks, which demonstrated the effectiveness of this approach in online reinforcement learning for vision-based tasks.
• A paper on data-driven multiscale topology optimization of spinodoid architected materials, which proposed a framework for automated computation of topological gradients and provided clear physical insights into material distribution.