@inproceedings{8e2b7807697a469cba9b2117f0713237,

title = "Marginalization on Bifurcation Diagrams: A New Perspective on Infinite-Horizon Prediction",

abstract = "This work addresses the problem of accurate infinite-horizon forecasting of dynamical systems with uncertain parameters. We introduce an intermediate representation of the probability distribution though the marginalization onto the sparse bifurcation structure of an ordinary differential equation (ODE) through integration over regions of attraction. With operations on this representation naturally completed in the space of parameter and state, metrics for the space of limiting behavior can be directly applied. Both limit points and limit cycles are investigated using the Hausdorff distance to treat them in a unified manner. The technique is further applied to stability detection, resulting in a likelihood ratio test. ",

keywords = "Forecasting, Infinite-Horizon, Nonlinear Dynamics, System Identification",

author = "Helmuth Naumer and Yizhen Lu and Farzad Kamalabadi",

note = "Funding Information: This work made use of the Illinois Campus Cluster, a computing resource that is operated by the Illinois Campus Cluster Program (ICCP) in conjunction with the National Center for Supercomputing Applications (NCSA) and which is supported by funds from the University of Illinois at Urbana-Champaign. Publisher Copyright: {\textcopyright} 2021 IEEE.; 21st IEEE Statistical Signal Processing Workshop, SSP 2021 ; Conference date: 11-07-2021 Through 14-07-2021",

year = "2021",

month = jul,

day = "11",

doi = "10.1109/SSP49050.2021.9513863",

language = "English (US)",

series = "IEEE Workshop on Statistical Signal Processing Proceedings",

publisher = "IEEE Computer Society",

pages = "431--435",

booktitle = "2021 IEEE Statistical Signal Processing Workshop, SSP 2021",

}