The Dynamics, Data and Deep Learning Workshop will bring together academic experts and industrial practitioners to think about new ways to discover, identify and augment mathematical models of dynamic processes using data in a rigorous and explainable fashion, and will focus on recent advancements at the interface of deep learning and dynamical systems. The covered topics will include mathematical concepts such as neural differential equations, Koopman and transfer spectral theory, rough path methodologies, (variational) autoencoders, invariant foliations, dynamic mode decomposition which are supplemented by various function approximators, like neural networks, compressed tensors, compressed sensing, etc. Parameter identification methods, such as online and/or stochastic optimisation techniques, sparse regression techniques could also be discussed to improve model accuracy. The workshop will also encourage discussion on application specific issues and tricks of the trade related to various computational implementations.
Neural differential equations (NDEs) have emerged as one of the central modelling frameworks in machine learning. In scientific applications, NDEs have shown great promise due to their ability to harness both the powerful approximation capabilities of neural networks and the continuous-time modelling of differential equations. This workshop aims to facilitate discussions between NDE researchers and leading experts at the interface of scientific modelling and data-driven machine learning.
Koopman and transfer spectral theory represents finite-dimensional nonlinear dynamical systems using an infinite-dimensional linear operator. This representation potentially enables easier prediction, estimation, and control of nonlinear systems. There have been numerous theoretical and algorithmic developments over the past decade, with many real-world applications. However, there remain many challenges. A goal is to discuss ideas (and cross-fertilisation of communities) to drive future progress in this field.
Rough path theory provides mathematical and computational tools for modelling the influence of continuous-time signals on dynamical systems. In recent years, it has started to play a key role in the design of state-of-the-art machine learning algorithms for processing noisy high-dimensional data streams in a wide range of contexts including finance, data assimilation, cybersecurity and medicine. Whilst rough paths have some known interactions with NDEs, the workshop intends to bring together researchers and broaden these connections – “sowing the seeds” for future interdisciplinary research.
The workshop is being organised by:
Peter Ashwin (University of Exeter)
Oscar Bandtlow (Queen Mary University of London)
Kim Batselier (TU Delft)
Emmanouil Benetos (Queen Mary University of London)
Nicolas Boullé (University of Cambridge)
Tim Dodwell (University of Exeter / Digilab)
Gonçalo dos Reis (University of Edinburgh)
Catherine Drysdale (University of Birmingham)
Nathan Kutz (University of Washington)
Maud Lemercier (University of Oxford)
Terry Lyons (University of Oxford)
Cristopher Salvi (Imperial College London)
Alexei Stepanenko (Cardiff University)
The workshop is taking place at Engineers’ House in Bristol.
Engineers’ House
The Promenade
Clifton Down
Avon
Bristol
BS8 3NB
For information on how to get there, please visit their website.
If you would like to attend this workshop, please fill out this form
