4 research outputs found
Elucidation of Relaxation Dynamics Beyond Equilibrium Through AI-informed X-ray Photon Correlation Spectroscopy
Understanding and interpreting dynamics of functional materials \textit{in
situ} is a grand challenge in physics and materials science due to the
difficulty of experimentally probing materials at varied length and time
scales. X-ray photon correlation spectroscopy (XPCS) is uniquely well-suited
for characterizing materials dynamics over wide-ranging time scales, however
spatial and temporal heterogeneity in material behavior can make interpretation
of experimental XPCS data difficult. In this work we have developed an
unsupervised deep learning (DL) framework for automated classification and
interpretation of relaxation dynamics from experimental data without requiring
any prior physical knowledge of the system behavior. We demonstrate how this
method can be used to rapidly explore large datasets to identify samples of
interest, and we apply this approach to directly correlate bulk properties of a
model system to microscopic dynamics. Importantly, this DL framework is
material and process agnostic, marking a concrete step towards autonomous
materials discovery
Asymptotic properties of a stochastic Lotka–Volterra model with infinite delay and regime switching
Abstract We investigate the long-term properties of a stochastic Lotka–Volterra model with infinite delay and Markovian chains on a finite state space. We investigate that the stochastic model admits a unique positive global solution which stays in the way of stochastically ultimate boundedness by constructing Lyapunov functions. Furthermore, the main results that the growth of the solution is slower than time under moderate condition and moment estimation in time average with the power p could be controlled are derived, which modified the known ones in recent literatures