Recurrent neural networks (RNNs) are powerful models for processing time-series data, but
it remains challenging to understand how they function. Improving this understanding is of …

The discovery of governing differential equations from data is an open frontier in machine
learning. The sparse identification of nonlinear dynamics (SINDy)\citep …

Understanding how neural dynamics give rise to behaviour is one of the most fundamental
questions in systems neuroscience. To achieve this, a common approach is to record neural …

The complexity of real-world geophysical systems is often compounded by the fact that the
observed measurements depend on hidden variables. These latent variables include …

We introduce a novel grid-independent model for learning partial differential equations
(PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a …

Systematic biases in numerical weather prediction models cause forecast deviation from
reality. While model biases also affect data assimilation and degrade the analysis accuracy …

Phytoplankton plays a key role in the carbon cycle and supports the oceanic food web.
While its seasonal and interannual cycles are rather well characterized owing to the modern …

Stochastic differential equations (SDEs) are one of the most important representations of
dynamical systems. They are notable for the ability to include a deterministic component of …

We introduce a novel grid-independent model for learning partial differential equations
(PDEs) from noisy and partial observations on irregular spatiotemporal grids. We propose a …

In chaotic dynamical systems such as the weather, prediction errors grow faster in some
situations than in others. Real‐time knowledge about the error growth could enable …