2016 |
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| 1. | Ujjwal, Sangeeta Rani; Punetha, Nirmal; Ramaswamy, Ram; Agrawal, Manish; Prasad, Awadhesh Driving-induced multistability in coupled chaotic oscillators: Symmetries and riddled basins Journal Article Chaos, 26 (6), 2016, ISSN: 10541500. Abstract | Links | BibTeX | Tags: Chaos, Multistability, Riddled Basins @article{Ujjwal2016a, title = {Driving-induced multistability in coupled chaotic oscillators: Symmetries and riddled basins}, author = {Sangeeta Rani Ujjwal and Nirmal Punetha and Ram Ramaswamy and Manish Agrawal and Awadhesh Prasad}, url = {http://dx.doi.org/10.1063/1.4954022}, doi = {10.1063/1.4954022}, issn = {10541500}, year = {2016}, date = {2016-01-01}, journal = {Chaos}, volume = {26}, number = {6}, abstract = {The human brain, power grids, arrays of coupled lasers and the Amazon rainforest are all characterized by multistability. The likelihood that these systems will remain in the most desirable of their many stable states depends on their stability against significant perturbations, particularly in a state space populated by undesirable states. Here we claim that the traditional linearization-based approach to stability is too local to adequately assess how stable a state is. Instead, we quantify it in terms of basin stability, a new measure related to the volume of the basin of attraction. Basin stability is non-local, nonlinear and easily applicable, even to high-dimensional systems. It provides a long-sought-after explanation for the surprisingly regular topologies of neural networks and power grids, which have eluded theoretical description based solely on linear stability. We anticipate that basin stability will provide a powerful tool for complex systems studies, including the assessment of multistable climatic tipping elements.}, keywords = {Chaos, Multistability, Riddled Basins}, pubstate = {published}, tppubtype = {article} } The human brain, power grids, arrays of coupled lasers and the Amazon rainforest are all characterized by multistability. The likelihood that these systems will remain in the most desirable of their many stable states depends on their stability against significant perturbations, particularly in a state space populated by undesirable states. Here we claim that the traditional linearization-based approach to stability is too local to adequately assess how stable a state is. Instead, we quantify it in terms of basin stability, a new measure related to the volume of the basin of attraction. Basin stability is non-local, nonlinear and easily applicable, even to high-dimensional systems. It provides a long-sought-after explanation for the surprisingly regular topologies of neural networks and power grids, which have eluded theoretical description based solely on linear stability. We anticipate that basin stability will provide a powerful tool for complex systems studies, including the assessment of multistable climatic tipping elements. |