Gang Yan (@eegyan) 's Twitter Profile
Gang Yan

@eegyan

Professor at Tongji University, Shanghai, China, with research interests in complex systems, data, AI.

ID: 23740597

linkhttp://gangyanlab.com calendar_today11-03-2009 09:19:11

61 Tweet

98 Followers

141 Following

Gang Yan (@eegyan) 's Twitter Profile Photo

Our paper is out now, in which we proposed an approach to infer the governing equation of complex network dynamics from incomplete and noisy data.

Physical Review X (@physrevx) 's Twitter Profile Photo

A machine-learning framework predicts when a complex system, such as an ecosystem or a power grid, will undergo a critical transition Read go.aps.org/3Wn4yUF Physics Magazine Viewpoint go.aps.org/4cYVcDY Zijia Liu čŒ¹å°ē£Š Tingting Gao Jack Murdoch Moore Gang Yan

A machine-learning framework predicts when a complex system, such as an ecosystem or a power grid, will undergo a critical transition
Read go.aps.org/3Wn4yUF
<a href="/PhysicsMagazine/">Physics Magazine</a> Viewpoint go.aps.org/4cYVcDY
<a href="/xwzliuzijia/">Zijia Liu</a> <a href="/RuXiao56423/">čŒ¹å°ē£Š</a> <a href="/TingtingGao314/">Tingting Gao</a> <a href="/JMurdochMoore/">Jack Murdoch Moore</a> <a href="/eegyan/">Gang Yan</a>
Nature Communications (@naturecomms) 's Twitter Profile Photo

.Gang Yan TingTing Gao BarzelLab propose an approach to infer the #stochastic differential equations of #complex systems from #experimental data. #GettingApplied nature.com/articles/s4146…

Gang Yan (@eegyan) 's Twitter Profile Photo

AI can predict tipping points before they happen - Glad to read the news article in ⁦The Economist⁩ about our research work recently published in ⁦Physical Review X⁩ journals.aps.org/prx/abstract/1… economist.com/science-and-te…

New Scientist (@newscientist) 's Twitter Profile Photo

AI can help predict when complex systems like the power grid or animal populations will hit a tipping point and abruptly transition to a radically different state. newscientist.com/article/244031…

Gang Yan (@eegyan) 's Twitter Profile Photo

Happy to share our recent work published in PRL Physical Review Letters - we discovered a geometric scaling law in the fruit fly neuronal networks across different developmental stages. Thanks for the highlight as Editors’ Suggestion and the nice viewpoint Featured in Physics.

Albert-LÔszló BarabÔsi (@barabasi) 's Twitter Profile Photo

Check out the #PRL2024 collection—two papers on Physical Networks! (promo.aps.org/PRL2024) Cory Glover ā€œMeasuring Entanglement in Physical Networksā€ (journals.aps.org/prl/abstract/1…) and Gang Yan's ā€œGeometric Scaling Law in Real Neuronal Networksā€ (journals.aps.org/prl/abstract/1…).

Gang Yan (@eegyan) 's Twitter Profile Photo

Happy to share our work just published in PRL Physical Review Letters - statistical inference is a widely used approach to reconstruct the hidden network from error-prone data. We find and prove that degree-heterogeneous networks are more reconstructable by it (than deg-homo counterparts)