Hosted by the EPSRC-funded m4DL research programme

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.

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