2017/02/03 by Mor Nitzan, Jose Casadiego, Marc Timme
Engineering · Neuroscience · Physics and Astronomy · #Asynchronous communication #Complex Network Analysis Techniques #Computer science #Dynamics (music) #Neural dynamics and brain function #Physics #Slime Mold and Myxomycetes Research #Statistical physics #Telecommunications #nlin.CD #physics.soc-ph
paper · pdf · doi:10.1126/sciadv.1600396
published as Science Advances, 2017, Vol. 3, no. 2, e160039 · 7 pages, 5 figures
openalex publication_date 2017/02/03 · arxiv created 2018/01/17 · arxiv updated 2018/01/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
Revealing physical interactions in complex systems from observed collective dynamics constitutes a fundamental inverse problem in science. Current reconstruction methods require access to a system's model or dynamical data at a level of detail often not available. We exploit changes in invariant measures, in particular distributions of sampled states of the system in response to driving signals, and use compressed sensing to reveal physical interaction networks. Dynamical observations following driving suffice to infer physical connectivity even if they are temporally disordered, are acquired at large sampling intervals, and stem from different experiments. Testing various nonlinear dynamic processes emerging on artificial and real network topologies indicates high reconstruction quality for existence as well as type of interactions. These results advance our ability to reveal physical interaction networks in complex synthetic and natural systems.