A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
Constructing neural network based models for simulating dynamical systems
Dynamical systems see widespread use in natural sciences like physics, biology, and
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …
chemistry, as well as engineering disciplines such as circuit analysis, computational fluid …
Hyena hierarchy: Towards larger convolutional language models
Recent advances in deep learning have relied heavily on the use of large Transformers due
to their ability to learn at scale. However, the core building block of Transformers, the …
to their ability to learn at scale. However, the core building block of Transformers, the …
On neural differential equations
P Kidger - arXiv preprint arXiv:2202.02435, 2022 - arxiv.org
The conjoining of dynamical systems and deep learning has become a topic of great
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
interest. In particular, neural differential equations (NDEs) demonstrate that neural networks …
Neural controlled differential equations for irregular time series
Neural ordinary differential equations are an attractive option for modelling temporal
dynamics. However, a fundamental issue is that the solution to an ordinary differential …
dynamics. However, a fundamental issue is that the solution to an ordinary differential …
Graph neural ordinary differential equations
We introduce the framework of continuous--depth graph neural networks (GNNs). Graph
neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs …
neural ordinary differential equations (GDEs) are formalized as the counterpart to GNNs …
Neural sdes as infinite-dimensional gans
Stochastic differential equations (SDEs) are a staple of mathematical modelling of temporal
dynamics. However, a fundamental limitation has been that such models have typically been …
dynamics. However, a fundamental limitation has been that such models have typically been …
Learning dynamic alignment via meta-filter for few-shot learning
Abstract Few-shot learning (FSL), which aims to recognise new classes by adapting the
learned knowledge with extremely limited few-shot (support) examples, remains an …
learned knowledge with extremely limited few-shot (support) examples, remains an …
Structural identification with physics-informed neural ordinary differential equations
This paper exploits a new direction of structural identification by means of Neural Ordinary
Differential Equations (Neural ODEs), particularly constrained by domain knowledge, such …
Differential Equations (Neural ODEs), particularly constrained by domain knowledge, such …
Liquid structural state-space models
A proper parametrization of state transition matrices of linear state-space models (SSMs)
followed by standard nonlinearities enables them to efficiently learn representations from …
followed by standard nonlinearities enables them to efficiently learn representations from …