Physical Networks
Physical networks are networks of tangible objects, in which nodes and edges are embedded in physical space and subject to constraints such as volume exclusion [(Dehmamy, Milanlouei, and Barabási 2018)]. Examples include biological neural networks, vascular systems, porous media, and granular materials. Advances in imaging technologies and accelerated reconstruction by ML/AI have recently made it possible to obtain detailed three-dimensional maps of such complex systems, providing new opportunities for studying physical networks.
In recent years, the physical embedding of networks has been shown to give rise to emergent phenomena, including entanglement [(Liu, Dehmamy, and Barabási 2021), (Glover and Barabási 2024)], bundling (Bonamassa et al. 2024), jamming (Pósfai et al. 2024), and correlations between node degree and node volume (Pete et al. 2024).
For my PhD thesis, I am exploring the spectral and topological properties of physical networks, as well as the dynamical processes that unfold on them.
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