Outputs (10)

The nonequilibrium potential today: A short review (2022)
Journal Article

A brief review is made of the birth and evolution of the “nonequilibrium potential” (NEP) concept. As if providing a landscape for qualitative reasoning were not helpful enough, the NEP adds a quantitative dimension to the qualitative theory of diffe... Read More about The nonequilibrium potential today: A short review.

Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models (2022)
Presentation / Conference Contribution

The forecasting of large ramps in wind power output known as ramp events is crucial for the incorporation of large volumes of wind energy into national electricity grids. Large variations in wind power supply must be compensated by ancillary energy s... Read More about Integrating wind variability to modelling wind-ramp events using a non-binary ramp function and deep learning models.

Task-oriented dialogue systems: Performance vs. quality-optima, a review (2022)
Presentation / Conference Contribution

Task-oriented dialogue systems (TODS) are continuing to rise in popularity as various industries find ways to effectively harness their capabilities, saving both time and money. However, even state-of-the-art TODS are not yet reaching their full pote... Read More about Task-oriented dialogue systems: Performance vs. quality-optima, a review.

qNoise: A generator of non-Gaussian colored noise (2022)
Journal Article

We introduce a software generator for a class of colored (self-correlated) and non-Gaussian noise, whose statistics and spectrum depend on two param- eters, q and τ. Inspired by Tsallis’ nonextensive formulation of statistical physics, the so-called... Read More about qNoise: A generator of non-Gaussian colored noise.

Wind power ramp characterisation and forecasting using Numerical Weather Prediction and Machine Learning models (2021)
Presentation / Conference Contribution

A nonequilibrium-potential approach to competition in neural populations (2019)
Journal Article

Energy landscapes are a highly useful aid for the understanding of dynamical systems, and a particularly valuable tool for their analysis. For a broad class of rate neural- network models of relevance in neuroscience, we derive a global Lyapunov func... Read More about A nonequilibrium-potential approach to competition in neural populations.

Large-scale atmospheric phenomena under the lens of ordinal time-series analysis and information theory measures (2017)
Book Chapter

This review presents a synthesis of our work done in the framework of the European project Learning about Interacting Networks in Climate (LINC, climatelinc.eu). We have applied tools of information theory and ordinal time series analysis to investig... Read More about Large-scale atmospheric phenomena under the lens of ordinal time-series analysis and information theory measures.

Assessing the direction of climate interactions by means of complex networks and information theoretic tools (2015)
Journal Article

© 2015 AIP Publishing LLC. An estimate of the net direction of climate interactions in different geographical regions is made by constructing a directed climate network from a regular latitude-longitude grid of nodes, using a directionality index (DI... Read More about Assessing the direction of climate interactions by means of complex networks and information theoretic tools.

Distinguishing the effects of internal and forced atmospheric variability in climate networks (2014)
Journal Article

The fact that the climate on the earth is a highly complex dynamical system is well-known. In the last few decades great deal of effort has been focused on understanding how climate phenomena in one geographical region affects the climate of other re... Read More about Distinguishing the effects of internal and forced atmospheric variability in climate networks.

Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales (2013)
Journal Article

We study global climate networks constructed by means of ordinal time series analysis. Climate interdependencies among the nodes are quantified by the mutual information, computed from time series of monthly-averaged surface air temperature anomalies... Read More about Inferring interdependencies in climate networks constructed at inter-annual, intra-season and longer time scales.