We report here a full-dimensional machine learning global potential surface (PES) for the
rearrangement of methylhydroxycarbene (H3C–C–OH, 1t). The PES is trained with the …

In this paper, we study baroclinic waves from both the experimental and the theoretical
perspective. We obtain data from a rotating annulus experiment capable of producing a …

In this paper, we unveil the geometrical template of phase space structures that governs
transport in a Hamiltonian system described by a potential energy surface with an …

The Maslov index of a periodic orbit is an important piece in the semiclassical quantization
of nonintegrable systems, while almost all existing techniques that lead to a rigorous …

In this paper we bring together the method of Lagrangian descriptors and the principle of
least action, or more precisely, of stationary action, in both deterministic and stochastic …

Lagrangian descriptors provide a global dynamical picture of the geometric structures for
arbitrarily time-dependent flows with broad applications. This paper develops a …

This paper discusses the use of Lagrangian and Hamiltonian dynamics as alternative
approaches for understanding the motion of objects in classical mechanics. These …

In this paper we investigate the use of Machine Learning techniques, specifically Support
Vector Machines (SVMs), to classify chaotic and regular trajectories in Hamiltonian systems …

In this paper, we analyse the classical action as a tool to reveal the phase space structure of
Hamiltonian systems simply and intuitively. We construct a scalar field using the values of …

In this paper we demonstrate the capability of the method of Lagrangian descriptors to unveil
the phase space structures that characterize transport in high-dimensional symplectic maps …