Using machine learning to predict catastrophes in dynamical systems |
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Authors: | Jesse Berwald,Tomá &scaron Gedeon |
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Affiliation: | a Department of Mathematical Sciences, Montana State University, MT, USAb Department of Computer Science, Montana State University, MT, USA |
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Abstract: | Nonlinear dynamical systems, which include models of the Earth’s climate, financial markets and complex ecosystems, often undergo abrupt transitions that lead to radically different behavior. The ability to predict such qualitative and potentially disruptive changes is an important problem with far-reaching implications. Even with robust mathematical models, predicting such critical transitions prior to their occurrence is extremely difficult. In this work, we propose a machine learning method to study the parameter space of a complex system, where the dynamics is coarsely characterized using topological invariants. We show that by using a nearest neighbor algorithm to sample the parameter space in a specific manner, we are able to predict with high accuracy the locations of critical transitions in parameter space. |
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Keywords: | 65P99 37B30 68T05 37N99 |
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