The field of computational methods for engineering and shape analysis is witnessing a significant shift towards the development of innovative and efficient architectures. Researchers are exploring new approaches to improve the performance and accuracy of existing methods, such as the use of transformers and deep learning techniques to analyze and optimize complex structures. One of the key directions is the integration of data-driven methods with traditional techniques to enhance the accuracy and robustness of results. Another area of focus is the development of uncertainty-informed and probabilistic methods to handle the inherent uncertainties in engineering systems. These advancements have the potential to revolutionize the field by enabling the design of highly efficient and complex structures, and accurately estimating the probability of failure in engineering systems under uncertainty. Noteworthy papers include: HodgeFormer, which proposes a novel transformer-based architecture for learnable operators on triangular meshes, and Deep Inverse Rosenblatt Transport, which introduces a framework for reliability analysis in solid mechanics. GUIDe is also a notable work, which presents a generative and uncertainty-informed inverse design framework for on-demand nonlinear functional responses. Additionally, Transformer-based Topology Optimization is a significant contribution, which proposes a machine learning model for topology optimization that embeds critical boundary and loading conditions directly into the tokenized domain representation.