26 research outputs found
Koopman Reduced Order Modeling with Confidence Bounds
This paper introduces a reduced order modeling technique based on Koopman
operator theory that gives confidence bounds on the model's predictions. It is
based on a data-driven spectral decomposition of said operator. The reduced
order model is constructed using a finite number of Koopman eigenvalues and
modes while the rest of spectrum is treated as a noise process. This noise
process is used to extract the confidence bounds. Additionally, we propose a
heuristic algorithm to choose the number of deterministic modes to keep in the
model. We assume Gaussian observational noise in our models. As the number of
modes used for the reduced order model increases, we approach a deterministic
plus Gaussian noise model. The Gaussian-ity of the noise can be measured via a
Shapiro-Wilk test. As the number of modes increase, the modal noise better
approximates a Gaussian distribution. As the number of modes increases past the
threshold, the standard deviation of the modal distribution decreases rapidly.
This allows us to propose a heuristic algorithm for choosing the number of
deterministic modes to keep for the model.Comment: Extended abstract, updated figures, small changes to tex
Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from data as
a linear combination of SVR models at a range of scales, starting at a coarse
scale and moving to finer scales as the hierarchy continues. In the original
formulation of HSVR, there were no rules for choosing the depth of the model.
In this paper, we observe in a number of models a phase transition in the
training error -- the error remains relatively constant as layers are added,
until a critical scale is passed, at which point the training error drops close
to zero and remains nearly constant for added layers. We introduce a method to
predict this critical scale a priori with the prediction based on the support
of either a Fourier transform of the data or the Dynamic Mode Decomposition
(DMD) spectrum. This allows us to determine the required number of layers prior
to training any models.Comment: 18 pages, 9 figure
Spectral Complexity of Directed Graphs and Application to Structural Decomposition
We introduce a new measure of complexity (called spectral complexity) for
directed graphs. We start with splitting of the directed graph into its
recurrent and non-recurrent parts. We define the spectral complexity metric in
terms of the spectrum of the recurrence matrix (associated with the reccurent
part of the graph) and the Wasserstein distance. We show that the total
complexity of the graph can then be defined in terms of the spectral
complexity, complexities of individual components and edge weights. The
essential property of the spectral complexity metric is that it accounts for
directed cycles in the graph. In engineered and software systems, such cycles
give rise to sub-system interdependencies and increase risk for unintended
consequences through positive feedback loops, instabilities, and infinite
execution loops in software. In addition, we present a structural decomposition
technique that identifies such cycles using a spectral technique. We show that
this decomposition complements the well-known spectral decomposition analysis
based on the Fiedler vector. We provide several examples of computation of
spectral and total complexities, including the demonstration that the
complexity increases monotonically with the average degree of a random graph.
We also provide an example of spectral complexity computation for the
architecture of a realistic fixed wing aircraft system.Comment: We added new theoretical results in Section 2 and introduced a new
section 2.2 devoted to intuitive and physical explanations of the concepts
from the pape
Extended Dynamic Mode Decomposition with Learned Koopman Eigenfunctions for Prediction and Control
This paper presents a novel learning framework to construct Koopman eigenfunctions for unknown, nonlinear dynamics using data gathered from experiments. The learning framework can extract spectral information from the full non-linear dynamics by learning the eigenvalues and eigenfunctions of the associated Koopman operator. We then exploit the learned Koopman eigenfunctions to learn a lifted linear state-space model. To the best of our knowledge, our method is the first to utilize Koopman eigenfunctions as lifting functions for EDMD-based methods. We demonstrate the performance of the framework in state prediction and closed loop trajectory tracking of a simulated cart pole system. Our method is able to significantly improve the controller performance while relying on linear control methods to do nonlinear control
A Koopman Operator-Based Prediction Algorithm and its Application to COVID-19 Pandemic
The problem of prediction of behavior of dynamical systems has undergone a
paradigm shift in the second half of the 20th century with the discovery of the
possibility of chaotic dynamics in simple, physical, dynamical systems for
which the laws of evolution do not change in time. The essence of the paradigm
is the long term exponential divergence of trajectories. However, that paradigm
does not account for another type of unpredictability: the ``Black Swan" event.
It also does not account for the fact that short-term prediction is often
possible even in systems with exponential divergence. In our framework, the
Black Swan type dynamics occurs when an underlying dynamical system suddenly
shifts between dynamics of different types. A learning and prediction system
should be capable of recognizing the shift in behavior, exemplified by
``confidence loss". In this paradigm, the predictive power is assessed
dynamically and confidence level is used to switch between long term prediction
and local-in-time prediction. Here we explore the problem of prediction in
systems that exhibit such behavior. The mathematical underpinnings of our
theory and algorithms are based on an operator-theoretic approach in which the
dynamics of the system are embedded into an infinite-dimensional space. We
apply the algorithm to a number of case studies including prediction of
influenza cases and the COVID-19 pandemic. The results show that the predictive
algorithm is robust to perturbations of the available data, induced for example
by delays in reporting or sudden increase in cases due to increase in testing
capability. This is achieved in an entirely data-driven fashion, with no
underlying mathematical model of the disease
The Redistribution of Power: Neurocardiac Signaling, Alcohol and Gender
Human adaptability involves interconnected biological and psychological control processes that determine how successful we are in meeting internal and environmental challenges. Heart rate variability (HRV), the variability in consecutive R-wave to R-wave intervals (RRI) of the electrocardiogram, captures synergy between the brain and cardiovascular control systems that modulate adaptive responding. Here we introduce a qualitatively new dimension of adaptive change in HRV quantified as a redistribution of spectral power by applying the Wasserstein distance with exponent 1 metric (W1) to RRI spectral data. We further derived a new index, D, to specify the direction of spectral redistribution and clarify physiological interpretation. We examined gender differences in real time RRI spectral power response to alcohol, placebo and visual cue challenges. Adaptive changes were observed as changes in power of the various spectral frequency bands (i.e., standard frequency domain HRV indices) and, during both placebo and alcohol intoxication challenges, as changes in the structure (shape) of the RRI spectrum, with a redistribution towards lower frequency oscillations. The overall conclusions from the present study are that the RRI spectrum is capable of a fluid and highly flexible response, even when oscillations (and thus activity at the sinoatrial node) are pharmacologically suppressed, and that low frequency oscillations serve a crucial but less studied role in physical and mental health
An agent-based model of urban insurgence: Effect of gathering sites and Koopman mode analysis.
The paper investigates the effect of preferential gathering sites on urban insurgency in an agent-based model (ABM). The ABM model was proposed in earlier work and has been validated using FBI data. There is a nonlinear tradeoff between the local density of citizens due to the number of preferential gathering sites and the ability of law enforcement officers (LEOs) to adequately patrol that leads to a non-monotonic behavior in the number of large scale outburst of insurgency with respect to the number of gathering sites. The inclusion of a moderate number of sites decreases the number of large-scale outbursts. Having no gathering sites or a large number of gathering sites has a dilutive effect on the number of large-scale outbursts. Thus, this non-monotonicity indicates that a small number of organized units produces a larger insurgency effect than a larger number of distributed units. It is also shown, using Koopman mode analysis, that the spatial morphology of agents due to the gathering sites gives rise to temporal organization of the model dynamics; there is a prominent quasi-periodic component in the number of active and intimidated citizens and in the spatial distribution of the LEOs