Towards data-driven discovery of governing equations in geosciences
Governing equations are foundations for modelling, predicting, and understanding the Earth
system. The Earth system is undergoing rapid change, and the conventional approaches for …
system. The Earth system is undergoing rapid change, and the conventional approaches for …
System identification based on characteristic curves: a mathematical connection between power series and Fourier analysis for first-order nonlinear systems
FJ Gonzalez - Nonlinear Dynamics, 2024 - Springer
Recently, the s inosoidal o utput r esponse in p ower s eries (SORPS) formalism was
presented for system identification and simulation. Based on the concept of c haracteristic c …
presented for system identification and simulation. Based on the concept of c haracteristic c …
Data-Driven Discovery of Unmanned Aerial Vehicles Dynamics*
This paper presents Wide-Array of Nonlinear Dynamics Approximation (WyNDA) as a
method for discovering the governing equations of Unmanned Aerial Vehicles (UAVs) …
method for discovering the governing equations of Unmanned Aerial Vehicles (UAVs) …
[HTML][HTML] Modelling pellet size and shape evolution during the breakage stage in spheronisation
J Whelan-Smith, MS How, SL Rough, L Wang… - Powder Technology, 2024 - Elsevier
In the breakage stage of extrusion-spheronisation, initially cylindrical extrudates undergo
simultaneous breakage and rounding on a rotating friction plate. This sets the starting …
simultaneous breakage and rounding on a rotating friction plate. This sets the starting …
[HTML][HTML] Discovering PDEs Corrections from Data Within a Hybrid Modeling Framework
C Ghnatios, F Chinesta - Mathematics, 2024 - mdpi.com
In the context of hybrid twins, a data-driven enrichment is added to the physics-based
solution to represent with higher accuracy the reference solution assumed to be known at …
solution to represent with higher accuracy the reference solution assumed to be known at …
Active Symbolic Discovery of Ordinary Differential Equations via Phase Portrait Sketching
Discovering Ordinary Differential Equations (ODEs) from trajectory data is a crucial task in AI-
driven scientific discovery. Recent methods for symbolic discovery of ODEs primarily rely on …
driven scientific discovery. Recent methods for symbolic discovery of ODEs primarily rely on …
[HTML][HTML] Data driven modeling of heavy-duty joint system for DEMO manipulators: An initial study from MPD joint simulation
This study investigates the subspace modeling method in developing surrogate model of a
heavy-duty joint system in a muti-purpose deployer. The joint system is constructed in the …
heavy-duty joint system in a muti-purpose deployer. The joint system is constructed in the …
Automating the Discovery of Partial Differential Equations in Dynamical Systems
W Li, R Carvalho - arXiv preprint arXiv:2404.16444, 2024 - arxiv.org
Identifying partial differential equations (PDEs) from data is crucial for understanding the
governing mechanisms of natural phenomena, yet it remains a challenging task. We present …
governing mechanisms of natural phenomena, yet it remains a challenging task. We present …
Data-Efficient System Identification via Lipschitz Neural Networks
Extracting dynamic models from data is of enormous importance in understanding the
properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of …
properties of unknown systems. In this work, we employ Lipschitz neural networks, a class of …
[PDF][PDF] Finding commonalities in dynamical systems with gaussian processes
A Besginow, JD Hüwel… - DataNinja …, 2024 - biecoll.ub.uni-bielefeld.de
Finding Commonalities in Dynamical Systems with Gaussian Processes Page 1
Proceedings of the DataNinja sAIOnARA 2024 Conference 26-28 DOI: 10.11576/dataninja-1162 …
Proceedings of the DataNinja sAIOnARA 2024 Conference 26-28 DOI: 10.11576/dataninja-1162 …