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 …
Robust flow control and optimal sensor placement using deep reinforcement learning
This paper focuses on finding a closed-loop strategy to reduce the drag of a cylinder in
laminar flow conditions. Deep reinforcement learning algorithms have been implemented to …
laminar flow conditions. Deep reinforcement learning algorithms have been implemented to …
Understanding the role of sensor optimisation in complex systems
B Suslu, F Ali, IK Jennions - Sensors, 2023 - mdpi.com
Complex systems involve monitoring, assessing, and predicting the health of various
systems within an integrated vehicle health management (IVHM) system or a larger system …
systems within an integrated vehicle health management (IVHM) system or a larger system …
Determinant-based fast greedy sensor selection algorithm
In this paper, the sparse sensor placement problem for least-squares estimation is
considered, and the previous novel approach of the sparse sensor selection algorithm is …
considered, and the previous novel approach of the sparse sensor selection algorithm is …
Sensor selection by greedy method for linear dynamical systems: Comparative study on Fisher-information-matrix, observability-Gramian and Kalman-filter-based …
Objective functions for sensor selection are investigated in linear time-invariant systems with
a large number of sensor candidates. This study compared the performance of sensor sets …
a large number of sensor candidates. This study compared the performance of sensor sets …
Efficient sensor node selection for observability Gramian optimization
Optimization approaches that determine sensitive sensor nodes in a large-scale, linear time-
invariant, and discrete-time dynamical system are examined under the assumption of …
invariant, and discrete-time dynamical system are examined under the assumption of …
Seismic wavefield reconstruction based on compressed sensing using data-driven reduced-order model
Reconstruction of the distribution of ground motion due to an earthquake is one of the key
technologies for the prediction of seismic damage to infrastructure. Particularly, the …
technologies for the prediction of seismic damage to infrastructure. Particularly, the …
Optimization of sparse sensor placement for estimation of wind direction and surface pressure distribution using time-averaged pressure-sensitive paint data on …
This study proposes a method for predicting the wind direction against the simple
automobile model (Ahmed model) and the surface pressure distributions on it by using data …
automobile model (Ahmed model) and the surface pressure distributions on it by using data …
Data-driven sensor selection method based on proximal optimization for high-dimensional data with correlated measurement noise
The present paper proposes a data-driven sensor selection method for a high-dimensional
nondynamical system with strongly correlated measurement noise. The proposed method is …
nondynamical system with strongly correlated measurement noise. The proposed method is …
Data-driven approximations of dynamical systems operators for control
Abstract The Koopman and Perron Frobenius transport operators are fundamentally
changing how we approach dynamical systems, providing linear representations for even …
changing how we approach dynamical systems, providing linear representations for even …