2020-12-07 ~ 2020-12-11 768
Dates: 7-11 December 2020
Dealing with the connectivity and transformation of different compo- nents in a space, topology provides a dramatic simplification of geometric complexity. Persistent homology, a new branch of algebraic topology, is able to bridge the gap between traditional topology and geometry by incorporating certain geometric features into topological invariants. Computational topology promises potentially revolutionary approaches to physical sciences. For example, persistent homology has had tremendous success in the abstraction and simplification of macromolecular structural complexity and drug discovery. Topological representations offer an excellent pre-condition for the machine learning of massive complex datasets and images. In computer science, algebraic topology is applied to concurrency, distributed computing, sequential computing and networking. In general, computational topology has found interesting applications in physics, chemistry, biology, material science, fluid mechanics, computer graphics, control theory, geometric design, shape analysis. This success has considerably boosted related mathematical fields, including computational geometry, differential geometry, spectral geometry, geometric topology, geometric algebra, combinatorics, partial differential equation, optimization, inverse problem, and statistics.
The proposed workshop, Computational Topology and Application, will bring together researchers from mathematics, physics, chemistry, biology, and computer science to explore new ways to bridge these diverse disciplines, and to facilitate the use of topology for various applications, including mathematical areas.
Professor Yusu Wang, Ohio State University,
Professor Guowei Wei, Michigan State University
Professor Jie Wu, Hebei Normal University