Deep reinforcement learning (DRL) has achieved significant results in many machine
learning (ML) benchmarks. In this short survey, we provide an overview of DRL applied to …

In recent years, machine-learning techniques, particularly deep learning, have outperformed
traditional time-series forecasting approaches in many contexts, including univariate and …

Surrogate models are necessary to optimize meaningful quantities in physical dynamics as
their recursive numerical resolutions are often prohibitively expensive. It is mainly the case …

How can we tell whether two neural networks utilize the same internal processes for a
particular computation? This question is pertinent for multiple subfields of neuroscience and …

This paper investigates the use of data-driven methods for the reconstruction of unsteady
fluid flow fields. The proposed framework is based on the combination of machine learning …

This paper discusses the chaos prediction of nonlinear systems using various neural
networks based on the modified substructure data-driven modeling architecture. In the …

Dynamical systems that evolve continuously over time are ubiquitous throughout science
and engineering. Machine learning (ML) provides data-driven approaches to model and …

An industrial process usually has multiple operating conditions or periods due to various
factors such as the fluctuation of raw material quality, differences of worker levels, and …

Nonlinear dynamical systems with control parameters may not be well modeled by shallow
neural networks. In this paper, the stable fixed-point solutions, periodic and chaotic solutions …

Prediction of nonlinear and dynamic systems is a challenging task, however with the aid of
machine learning techniques, particularly neural networks, is now possible to accomplish …