Modern Koopman theory for dynamical systems
The field of dynamical systems is being transformed by the mathematical tools and
algorithms emerging from modern computing and data science. First-principles derivations …
algorithms emerging from modern computing and data science. First-principles derivations …
Graph neural network and Koopman models for learning networked dynamics: A comparative study on power grid transients prediction
Continuous monitoring of the spatio-temporal dynamic behavior of critical infrastructure
networks, such as the power systems, is a challenging but important task. In particular …
networks, such as the power systems, is a challenging but important task. In particular …
Equivariance and partial observations in Koopman operator theory for partial differential equations
The Koopman operator has become an essential tool for datadriven analysis, prediction,
and control of complex systems. The main reason is the enormous potential of identifying …
and control of complex systems. The main reason is the enormous potential of identifying …
Data-driven stabilization of discrete-time control-affine nonlinear systems: A Koopman operator approach
In recent years data-driven analysis of dynamical systems has attracted a lot of attention and
transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being …
transfer operator techniques, namely, Perron-Frobenius and Koopman operators are being …
Analysis and prediction of human mobility in the United States during the early stages of the COVID-19 pandemic using regularized linear models
M Chakraborty, M Shakir Mahmud… - Transportation …, 2023 - journals.sagepub.com
Since the United States started grappling with the COVID-19 pandemic, with the highest
number of confirmed cases and deaths in the world as of August 2020, most states have …
number of confirmed cases and deaths in the world as of August 2020, most states have …
Data-driven operator theoretic methods for global phase space learning
SP Nandanoori, S Sinha… - 2020 American Control …, 2020 - ieeexplore.ieee.org
In this work, we developed new Koopman operator techniques to explore the global phase
space of a nonlinear system from time-series data. In particular, we address the problem of …
space of a nonlinear system from time-series data. In particular, we address the problem of …
Online real-time learning of dynamical systems from noisy streaming data
S Sinha, SP Nandanoori, DA Barajas-Solano - Scientific Reports, 2023 - nature.com
Recent advancements in sensing and communication facilitate obtaining high-frequency
real-time data from various physical systems like power networks, climate systems …
real-time data from various physical systems like power networks, climate systems …
Application of advanced causal analyses to identify processes governing secondary organic aerosols
Understanding how different physical and chemical atmospheric processes affect the
formation of fine particles has been a persistent challenge. Inferring causal relations …
formation of fine particles has been a persistent challenge. Inferring causal relations …
Dynamics harmonic analysis of robotic systems: Application in data-driven koopman modelling
We introduce the use of harmonic analysis to decompose the state space of symmetric
robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces …
robotic systems into orthogonal isotypic subspaces. These are lower-dimensional spaces …
Multi-level optimization with the koopman operator for data-driven, domain-aware, and dynamic system security
Abstract Cyber–Physical Systems (CPSs) like the power grid are critically important but also
increasingly vulnerable; ensuring reliable system operation in the face of disruptions is …
increasingly vulnerable; ensuring reliable system operation in the face of disruptions is …