[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 …
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 …
Computational Approach covers the development and application of methods for the …
Analysis of two dimensional, wide-band, bistable vibration energy harvester
In this paper a new strategy for developing broadband, bi-directional, vibration energy
harvesters is presented. Often energy harvesting systems address unidirectional incoming …
harvesters is presented. Often energy harvesting systems address unidirectional incoming …
Composable text controls in latent space with odes
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 …
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
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 …
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 …
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
The inherent slow imaging speed of Magnetic Resonance Image (MRI) has spurred the
development of various acceleration methods, typically through heuristically undersampling …
development of various acceleration methods, typically through heuristically undersampling …
Revealing hidden dynamics from time-series data by ODENet
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 …
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 …
differential equations (PDEs) through complex pore geometries. Resolving spatiotemporal …
Dynamic parameter estimation with physics-based neural ordinary differential equations
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 …
model for dynamical studies and reliable operation of the power system. This paper …