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 …
Discovering causal relations and equations from data
Physics is a field of science that has traditionally used the scientific method to answer
questions about why natural phenomena occur and to make testable models that explain the …
questions about why natural phenomena occur and to make testable models that explain the …
[BOOK][B] Data-driven science and engineering: Machine learning, dynamical systems, and control
SL Brunton, JN Kutz - 2022 - books.google.com
Data-driven discovery is revolutionizing how we model, predict, and control complex
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
systems. Now with Python and MATLAB®, this textbook trains mathematical scientists and …
Data-driven aerospace engineering: reframing the industry with machine learning
Data science, and machine learning in particular, is rapidly transforming the scientific and
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
industrial landscapes. The aerospace industry is poised to capitalize on big data and …
Complex nonlinear dynamics and vibration suppression of conceptual airfoil models: A state-of-the-art overview
During the past few decades, several significant progresses have been made in exploring
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
complex nonlinear dynamics and vibration suppression of conceptual aeroelastic airfoil …
Challenges in dynamic mode decomposition
Dynamic mode decomposition (DMD) is a powerful tool for extracting spatial and temporal
patterns from multi-dimensional time series, and it has been used successfully in a wide …
patterns from multi-dimensional time series, and it has been used successfully in a wide …
Modeling of dynamical systems through deep learning
P Rajendra, V Brahmajirao - Biophysical Reviews, 2020 - Springer
This review presents a modern perspective on dynamical systems in the context of current
goals and open challenges. In particular, our review focuses on the key challenges of …
goals and open challenges. In particular, our review focuses on the key challenges of …
The multiverse of dynamic mode decomposition algorithms
MJ Colbrook - arxiv preprint arxiv:2312.00137, 2023 - arxiv.org
Dynamic Mode Decomposition (DMD) is a popular data-driven analysis technique used to
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …
decompose complex, nonlinear systems into a set of modes, revealing underlying patterns …
Coupled and uncoupled dynamic mode decomposition in multi-compartmental systems with applications to epidemiological and additive manufacturing problems
Abstract Dynamic Mode Decomposition (DMD) is an unsupervised machine learning
method that has attracted considerable attention in recent years owing to its equation-free …
method that has attracted considerable attention in recent years owing to its equation-free …
Sparsity promoting algorithm for identification of nonlinear dynamic system based on Unscented Kalman Filter using novel selective thresholding and penalty-based …
Identifying a nonlinear dynamic systems' governing equation is crucial for many engineering
applications, and yet a challenging task. In this study, the system's dynamics are …
applications, and yet a challenging task. In this study, the system's dynamics are …