[图书][B] Numerical methods for delay differential equations

A Bellen, M Zennaro - 2013 - books.google.com
The main purpose of the book is to introduce the readers to the numerical integration of the
Cauchy problem for delay differential equations (DDEs). Peculiarities and differences that …

[图书][B] Numerical methods for differential equations: a computational approach

JR Dormand - 2018 - taylorfrancis.com
With emphasis on modern techniques, Numerical Methods for Differential Equations: A
Computational Approach covers the development and application of methods for the …

Analysis of two dimensional, wide-band, bistable vibration energy harvester

B Andò, S Baglio, F Maiorca, C Trigona - Sensors and Actuators A …, 2013 - Elsevier
In this paper a new strategy for developing broadband, bi-directional, vibration energy
harvesters is presented. Often energy harvesting systems address unidirectional incoming …

Composable text controls in latent space with odes

G Liu, Z Feng, Y Gao, Z Yang, X Liang, J Bao… - arXiv preprint arXiv …, 2022 - arxiv.org
Real-world text applications often involve composing a wide range of text control operations,
such as editing the text wrt an attribute, manipulating keywords and structure, and …

Graph sequential neural ode process for link prediction on dynamic and sparse graphs

L Luo, G Haffari, S Pan - Proceedings of the Sixteenth ACM International …, 2023 - dl.acm.org
Link prediction on dynamic graphs is an important task in graph mining. Existing approaches
based on dynamic graph neural networks (DGNNs) typically require a significant amount of …

Derivation of efficient, continuous, explicit Runge–Kutta methods

B Owren, M Zennaro - SIAM journal on scientific and statistical computing, 1992 - SIAM
Continuous, explicit Runge–Kutta methods with the minimal number of stages are
considered. These methods are continuously differentiable if and only if one of the stages is …

Learning optimal k-space acquisition and reconstruction using physics-informed neural networks

W Peng, L Feng, G Zhao, F Liu - Proceedings of the IEEE …, 2022 - openaccess.thecvf.com
The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the
development of various acceleration methods, typically through heuristically undersampling …

Revealing hidden dynamics from time-series data by ODENet

P Hu, W Yang, Y Zhu, L Hong - Journal of Computational Physics, 2022 - Elsevier
To derive the hidden dynamics from observed data is one of the fundamental but also
challenging problems in many different fields. In this study, we propose a new type of …

Physics-constrained neural differential equations for learning multi-ionic transport

D Rehman, JH Lienhard - arXiv preprint arXiv:2303.04594, 2023 - arxiv.org
Continuum models for ion transport through polyamide nanopores require solving partial
differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal …

Dynamic parameter estimation with physics-based neural ordinary differential equations

X Kong, K Yamashita, B Foggo… - 2022 IEEE Power & …, 2022 - ieeexplore.ieee.org
Accurate estimation of dynamic parameters of gen-erators is crucial to building a reliable
model for dynamical studies and reliable operation of the power system. This paper …