Explores the principles of modularity and abstraction in computer systems design, emphasizing their role in simplifying complex systems and improving scalability.
Explores canonical transformations, their properties, and applications in Hamiltonian mechanics, emphasizing their role in simplifying the analysis of complex systems.
Delves into symbolic representation of state spaces using decision diagrams for high-level Petri nets, showcasing efficient encoding techniques and benchmark results.
Explores the role of higher-order topological properties in complex networks using topological data analysis for structural break and price anomaly detection.
Explores self-assembly of Microsystems, its importance, features, motivations, and examples in various fields, highlighting landmark achievements and future prospects.
