Benchmarking sparse system identification with low-dimensional chaos
Sparse system identification is the data-driven process of obtaining parsimonious differential
equations that describe the evolution of a dynamical system, balancing model complexity …
equations that describe the evolution of a dynamical system, balancing model complexity …
Learning nonlinear dynamics using kalman smoothing
JM Stevens-Haas, Y Bhangale, JN Kutz… - IEEE Access, 2024 - ieeexplore.ieee.org
Identifying Ordinary Differential Equations (ODEs) from measurement data requires both
fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data …
fitting the dynamics and assimilating, either implicitly or explicitly, the measurement data …
Multi-objective SINDy for parameterized model discovery from single transient trajectory data
J Lemus, B Herrmann - Nonlinear Dynamics, 2024 - Springer
The sparse identification of nonlinear dynamics (SINDy) has been established as an
effective technique to produce interpretable models of dynamical systems from time …
effective technique to produce interpretable models of dynamical systems from time …
SINDy-RL: Interpretable and Efficient Model-Based Reinforcement Learning
Deep reinforcement learning (DRL) has shown significant promise for uncovering
sophisticated control policies that interact in environments with complicated dynamics, such …
sophisticated control policies that interact in environments with complicated dynamics, such …
Uncertainty quantification in reduced‐order gas‐phase atmospheric chemistry modeling using ensemble SINDy
Uncertainty quantification during atmospheric chemistry modeling is computationally
expensive as it typically requires a large number of simulations using complex models. As …
expensive as it typically requires a large number of simulations using complex models. As …
Rapid Bayesian identification of sparse nonlinear dynamics from scarce and noisy data
We propose a fast probabilistic framework for identifying differential equations governing the
dynamics of observed data. We recast the SINDy method within a Bayesian framework and …
dynamics of observed data. We recast the SINDy method within a Bayesian framework and …
[HTML][HTML] Data-driven structural identification of nonlinear assemblies: Uncertainty Quantification
Nonlinear model identification from vibration data is challenging due to limited measured
data collected during the testing campaign and since the identified model should be capable …
data collected during the testing campaign and since the identified model should be capable …
Bayesian autoencoders for data-driven discovery of coordinates, governing equations and fundamental constants
Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy)
under ℓ 1 constraints allows joint discoveries of governing equations and latent coordinate …
under ℓ 1 constraints allows joint discoveries of governing equations and latent coordinate …
Sparse identification of nonlinear dynamics and Koopman operators with Shallow Recurrent Decoder Networks
Spatiotemporal modeling of real-world data poses a challenging problem due to inherent
high dimensionality, measurement noise, and expensive data collection procedures. In this …
high dimensionality, measurement noise, and expensive data collection procedures. In this …
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 …