Time-series machine learning techniques for modeling and identification of mechatronic systems with friction: A review and real application
S Ayankoso, P Olejnik - Electronics, 2023 - mdpi.com
Developing accurate dynamic models for various systems is crucial for optimization, control,
fault diagnosis, and prognosis. Recent advancements in information technologies and …
fault diagnosis, and prognosis. Recent advancements in information technologies and …
Automatically discovering ordinary differential equations from data with sparse regression
K Egan, W Li, R Carvalho - Communications Physics, 2024 - nature.com
Discovering nonlinear differential equations that describe system dynamics from empirical
data is a fundamental challenge in contemporary science. While current methods can …
data is a fundamental challenge in contemporary science. While current methods can …
Derivative-based SINDy (DSINDy): Addressing the challenge of discovering governing equations from noisy data
Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems
using state measurements. One approach, known as Sparse Identification of Nonlinear …
using state measurements. One approach, known as Sparse Identification of Nonlinear …
From biological data to oscillator models using SINDy
Periodic changes in the concentration or activity of different molecules regulate vital cellular
processes such as cell division and circadian rhythms. Developing mathematical models is …
processes such as cell division and circadian rhythms. Developing mathematical models is …
Reconstructing the Unsaturated Flow Equation From Sparse and Noisy Data: Leveraging the Synergy of Group Sparsity and Physics‐Informed Deep Learning
Data‐driven scientific discovery methods have been developed and applied to discover
governing equations from data, involving the attempt to discover the unsaturated flow …
governing equations from data, involving the attempt to discover the unsaturated flow …
Simultaneous identification and denoising of dynamical systems
In recent years there has been a push to discover the governing equations of dynamical
systems directly from measurements of the state, often motivated by systems that are too …
systems directly from measurements of the state, often motivated by systems that are too …
Data-driven reconstruction of limit cycle position provides side information for improved model identification with SINDy
Many important systems in nature are characterized by oscillations. To understand and
interpret such behavior, researchers use the language of mathematical models, often in the …
interpret such behavior, researchers use the language of mathematical models, often in the …
Enhancing model identification with SINDy via nullcline reconstruction
Many dynamical systems exhibit oscillatory behavior that can be modeled with differential
equations. Recently, these equations have increasingly been derived through data-driven …
equations. Recently, these equations have increasingly been derived through data-driven …
Constrained or Unconstrained? Neural-Network-Based Equation Discovery from Data
Throughout many fields, practitioners often rely on differential equations to model systems.
Yet, for many applications, the theoretical derivation of such equations and/or accurate …
Yet, for many applications, the theoretical derivation of such equations and/or accurate …
Statistical Mechanics of Dynamical System Identification
Recovering dynamical equations from observed noisy data is the central challenge of
system identification. We develop a statistical mechanical approach to analyze sparse …
system identification. We develop a statistical mechanical approach to analyze sparse …