The field of complex systems modeling is witnessing a significant shift towards the development of innovative methods that can efficiently capture the intricacies of real-world phenomena. Researchers are increasingly focusing on the integration of machine learning techniques with traditional modeling approaches to create more accurate and robust models. One notable direction is the use of neural cellular automata, which have shown great promise in modeling self-organizing processes and stochastic dynamics. Another area of interest is the application of generative models, such as diffusion-based models, to simulate complex systems and phenomena. These models have been successfully used to generate realistic images and simulate turbulence in fluid dynamics. Furthermore, there is a growing interest in the development of novel numerical methods, such as the Magnus method, to solve stochastic delay-differential equations and other complex mathematical models. Noteworthy papers in this area include the proposal of a novel PDE-driven corruption process for generative image synthesis, which generalizes existing PDE-based approaches and demonstrates improved diversity and quality of generated images. Additionally, the introduction of a stochastic framework for growth modeling and self-organization using mixtures of neural cellular automata has shown great potential in capturing the stochasticity of real-world biological and physical systems. The development of a latent score-based generative AI framework for learning stochastic, non-local closure models and constitutive laws in nonlinear dynamical systems has also been highlighted as a promising approach for modeling complex multiscale dynamical systems.