Abstract:
: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.
journal_name
Sci Advjournal_title
Science advancesauthors
Nitzan M,Casadiego J,Timme Mdoi
10.1126/sciadv.1600396subject
Has Abstractpub_date
2017-02-10 00:00:00pages
e1600396issue
2issn
2375-2548pii
1600396journal_volume
3pub_type
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