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Learning stable Koopman embeddings
In this paper, we present a new data-driven method for learning stable models of nonlinear
systems. Our model lifts the original state space to a higher-dimensional linear manifold …
systems. Our model lifts the original state space to a higher-dimensional linear manifold …
Learning stable models for prediction and control
In this article, we demonstrate the benefits of imposing stability on data-driven Koopman
operators. The data-driven identification of stable Koopman operators (DISKO) is …
operators. The data-driven identification of stable Koopman operators (DISKO) is …
Memory-efficient learning of stable linear dynamical systems for prediction and control
Abstract Learning a stable Linear Dynamical System (LDS) from data involves creating
models that both minimize reconstruction error and enforce stability of the learned …
models that both minimize reconstruction error and enforce stability of the learned …
Simba: System identification methods leveraging backpropagation
This manuscript details and extends the system identification methods leveraging the
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …
backpropagation (SIMBa) toolbox presented in previous work, which uses well-established …
Stable linear subspace identification: A machine learning approach
Machine Learning (ML) and linear System Identification (SI) have been historically
developed independently. In this paper, we leverage well-established ML tools—especially …
developed independently. In this paper, we leverage well-established ML tools—especially …
System norm regularization methods for Koopman operator approximation
Approximating the Koopman operator from data is numerically challenging when many
lifting functions are considered. Even low-dimensional systems can yield unstable or ill …
lifting functions are considered. Even low-dimensional systems can yield unstable or ill …
Nearest -stable matrix via Riemannian optimization
We study the problem of finding the nearest\varOmega Ω-stable matrix to a certain matrix A,
ie, the nearest matrix with all its eigenvalues in a prescribed closed set\varOmega Ω …
ie, the nearest matrix with all its eigenvalues in a prescribed closed set\varOmega Ω …
Learning stable Koopman embeddings for identification and control
F Fan, B Yi, D Rye, G Shi, IR Manchester - ar** in Learning Stable Linear Dynamics
When learning stable linear dynamical systems from data, three important properties are
desirable: i) predictive accuracy, ii) provable stability, and iii) computational efficiency …
desirable: i) predictive accuracy, ii) provable stability, and iii) computational efficiency …
[PDF][PDF] Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control
L Di Natale - 2024 - infoscience.epfl.ch
Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30%
of the global energy consumption. With traditional engineering solutions reaching their limits …
of the global energy consumption. With traditional engineering solutions reaching their limits …