Bias Variance

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Deep learning for early warning signals oftipping points

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new
and sometimes very different state. As the tipping point is
approached, the dynamics of complex and varied systems simplify down to a limited number of possible “normal forms” that
determine qualitative aspects of the new state that lies beyond
the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1
autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow
down near the transition. But they do not predict the nature
of the new state. Here we develop a deep learning algorithm
that provides EWS in systems it was not explicitly trained on, by
exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many
dynamical systems. The algorithm provides EWS in 268 empirical
and model time series from ecology, thermoacoustics, climatology,
and epidemiology with much greater sensitivity and specificity ...


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153 viewsBias Variance, edited  Sep 25, 2021 at 11:07

معرفی مقاله:

Deep learning for early warning signals oftipping points

Many natural systems exhibit tipping points where slowly changing environmental conditions spark a sudden shift to a new
and sometimes very different state. As the tipping point is
approached, the dynamics of complex and varied systems simplify down to a limited number of possible “normal forms” that
determine qualitative aspects of the new state that lies beyond
the tipping point, such as whether it will oscillate or be stable. In several of those forms, indicators like increasing lag-1
autocorrelation and variance provide generic early warning signals (EWS) of the tipping point by detecting how dynamics slow
down near the transition. But they do not predict the nature
of the new state. Here we develop a deep learning algorithm
that provides EWS in systems it was not explicitly trained on, by
exploiting information about normal forms and scaling behavior of dynamics near tipping points that are common to many
dynamical systems. The algorithm provides EWS in 268 empirical
and model time series from ecology, thermoacoustics, climatology,
and epidemiology with much greater sensitivity and specificity ...


#معرفی_مقاله #یادگیری_عمیق

🌴 سایت | 🌺 کانال | 🌳 پشتیبانی

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