Abstract Machine learning is rapidly becoming a core technology for scientific computing,
with numerous opportunities to advance the field of computational fluid dynamics. Here we …

Recent years have seen the rapid growth and development of the field of smart photonics,
where machine-learning algorithms are being matched to optical systems to add new …

Reservoir computing is a best-in-class machine learning algorithm for processing
information generated by dynamical systems using observed time-series data. Importantly, it …

Reservoir computing is a highly efficient network for processing temporal signals due to its
low training cost compared to standard recurrent neural networks, and generating rich …

Partial differential equations (PDEs) are among the most universal and parsimonious
descriptions of natural physical laws, capturing a rich variety of phenomenology and …

Machine learning (ML) encompasses a broad range of algorithms and modeling tools used
for a vast array of data processing tasks, which has entered most scientific disciplines in …

Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

Time-series analysis including forecasting is essential in a range of fields from finance to
engineering. However, long-term forecasting is difficult, particularly for cases where the …

Understanding the fundamental relationships between physics and its information-
processing capability has been an active research topic for many years. Physical reservoir …

We examine the efficiency of Recurrent Neural Networks in forecasting the spatiotemporal
dynamics of high dimensional and reduced order complex systems using Reservoir …