The field of computational modeling and representation learning is witnessing significant advancements, driven by the development of innovative architectures and techniques. A key direction in this field is the integration of multiscale modeling and representation learning, enabling the effective capture of complex patterns and relationships in diverse domains, such as seismic data analysis, road network representation, and network tomography. Notably, researchers are exploring the use of graph neural networks, transformer-based models, and hierarchical frequency-decomposition approaches to improve the accuracy and generalizability of their models. These advancements have the potential to revolutionize various applications, including intelligent transportation systems, seismic foundation modeling, and network performance estimation. Noteworthy papers include: SA-EMO, which proposes a novel Structure-Aligned Encoder-Mixture-of-Operators architecture for velocity-field inversion, achieving significant performance gains over traditional methods. Mesh-based Super-resolution of Detonation Flows with Multiscale Graph Transformers, which introduces a first-of-its-kind multiscale graph transformer approach for mesh-based super-resolution of reacting flows, demonstrating superior performance compared to traditional interpolation-based schemes.