#system identification
8 papers match
Numerical Spectrum Linking: Identification of Governing PDE via Koopman-Chebyshev Approximation with Resampling
Phonepaserth Sisaykeo, Shogo Muramatsu
The paper presents a numerical framework that uses Chebyshev spectral representations of Koopman operators to identify governing partial differential equations directly from observ…
Horizon Selection in Physics-Enhanced Neural ODEs: Theoretical Insights and Flux Linkage Application
Giulio Montecchio, Benjamin Hartmann, Sven Reimann +3
The paper investigates how the integration horizon used during training influences physics-enhanced Neural ODEs, proposing longer horizons to reduce bias in physical parameter esti…
RTS Smoother-Guided Learning of Physics-Based Neural Differential Models
Ahmet Demirkaya, Georgios Stratis, Tales Imbiriba +2
The paper introduces a hybrid neural‑physics framework that combines known ODE components with neural networks to learn missing dynamics, using a Rauch‑Tung‑Striebel smoother for l…
An Introduction to Sparse Identification of Nonlinear Dynamics for Engineering Applications
Yao Cheng Li, Ana Larrañaga, Steven L. Brunton +1
The paper presents a tutorial on the Sparse Identification of Nonlinear Dynamics (SINDy) method, showing how sparse regression can uncover interpretable governing equations from sm…
Control-Oriented System Identification: Classical, Learning, and Physics-Informed Approaches
S. Sivaranjani, Yuanyuan Shi, Nikolay Atanasov +6
The paper surveys classical, machine‑learning, and physics‑informed system identification methods that incorporate control‑relevant properties such as dissipativity and symmetry, d…
Optimum and Adaptive Complex-Valued Bilinear Filters
Bernhard Plaimer, Matthias Wagner, Oliver Lang +1
The paper extends real-valued bilinear adaptive filters to the complex domain and proposes several new complex-valued bilinear filters, evaluating their computational cost and perf…
Identifiability of Autonomous and Controlled Open Quantum Systems
Waqas Parvaiz, Johannes Aspman, Ales Wodecki +2
The paper studies how to determine (identify) the dynamics of autonomous and controlled open quantum systems by linking their master equations to classical linear and bilinear syst…
Learning to control switching nonlinear systems with Koopman operator regression
Edoardo Caldarelli, Oleksii Kachaiev, Cesare Molinari +1
The paper proposes using Koopman operator regression in a reproducing kernel Hilbert space to identify and control nonlinear systems with finite action spaces, creating a linear sw…
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