Exit time as a measure of ecological resilience
BMS Arani, SR Carpenter, L Lahti, EH Van Nes… - Science, 2021 - science.org
INTRODUCTION Financial markets may collapse, rainforest can shift to savanna, a person
can become trapped in a depression, and the Gulf Stream can come to a standstill. Such …
can become trapped in a depression, and the Gulf Stream can come to a standstill. Such …
Air quality prediction using optimal neural networks with stochastic variables
We apply recent methods in stochastic data analysis for discovering a set of few stochastic
variables that represent the relevant information on a multivariate stochastic system, used as …
variables that represent the relevant information on a multivariate stochastic system, used as …
Artificial intelligence and mathematical models of power grids driven by renewable energy sources: A survey
To face the impact of climate change in all dimensions of our society in the near future, the
European Union (EU) has established an ambitious target. Until 2050, the share of …
European Union (EU) has established an ambitious target. Until 2050, the share of …
jumpdiff: A Python library for statistical inference of jump-diffusion processes in observational or experimental data sets
We introduce a Python library, called jumpdiff, which includes all necessary functions to
assess jump-diffusion processes. This library includes functions which compute a set of non …
assess jump-diffusion processes. This library includes functions which compute a set of non …
Robust identification of harmonic oscillator parameters using the adjoint Fokker–Planck equation
We present a model-based output-only method for identifying from time series the
parameters governing the dynamics of stochastically forced oscillators. In this context …
parameters governing the dynamics of stochastically forced oscillators. In this context …
Learning interpretable dynamics of stochastic complex systems from experimental data
Complex systems with many interacting nodes are inherently stochastic and best described
by stochastic differential equations. Despite increasing observation data, inferring these …
by stochastic differential equations. Despite increasing observation data, inferring these …
Estimation of drift and diffusion functions from unevenly sampled time-series data
Complex systems can often be modeled as stochastic processes. However, physical
observations of such systems are often irregularly spaced in time, leading to difficulties in …
observations of such systems are often irregularly spaced in time, leading to difficulties in …
The Langevin approach: An R package for modeling Markov processes
We describe an R package developed by the research group Turbulence, Wind energy and
Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg, which extracts the …
Stochastics (TWiSt) at the Carl von Ossietzky University of Oldenburg, which extracts the …
Fatigue load estimation through a simple stochastic model
We propose a procedure to estimate the fatigue loads on wind turbines, based on a recent
framework used for reconstructing data series of stochastic properties measured at wind …
framework used for reconstructing data series of stochastic properties measured at wind …
Analysis and data-driven reconstruction of bivariate jump-diffusion processes
We introduce the bivariate jump-diffusion process, consisting of two-dimensional diffusion
and two-dimensional jumps, that can be coupled to one another. We present a data-driven …
and two-dimensional jumps, that can be coupled to one another. We present a data-driven …