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Found 20 papers, 9 papers with code
Spatio-temporal Patterns between ENSO and Weather-related Power Outages in the Continental United States
El Ni\~no-Southern Oscillation (ENSO) exhibits significant impacts on the frequency of extreme weather events and its socio-economic implications prevail on a global scale.
Effect of antibody levels on the spread of disease in multiple infections
We find that the antibody decay rate strongly affects the propagation process, and also that different network structures have different sensitivities to the antibody decay rate, and that changes in the antibody decay rate cause stronger changes in the propagation process in Barabasi Albert networks.
Predicting Instability in Complex Oscillator Networks: Limitations and Potentials of Network Measures and Machine Learning
A functional property that is of theoretical and practical interest for oscillatory systems is the stability of synchrony to localized perturbations.
SAMSGL: Series-Aligned Multi-Scale Graph Learning for Spatio-Temporal Forecasting
Despite the significant strides made by graph-based networks in spatio-temporal forecasting, there remain two pivotal factors closely related to forecasting performance that need further consideration: time delays in propagation dynamics and multi-scale high-dimensional interactions.
Embedding Theory of Reservoir Computing and Reducing Reservoir Network Using Time Delays
Reservoir computing (RC), a particular form of recurrent neural network, is under explosive development due to its exceptional efficacy and high performance in reconstruction or/and prediction of complex physical systems.
Symbiosis of an artificial neural network and models of biological neurons: training and testing
In this paper we show the possibility of creating and identifying the features of an artificial neural network (ANN) which consists of mathematical models of biological neurons.
PrEF: Percolation-based Evolutionary Framework for the diffusion-source-localization problem in large networks
We assume that the state of a number of nodes in a network could be investigated if necessary, and study what configuration of those nodes could facilitate a better solution for the diffusion-source-localization (DSL) problem.
Predicting Basin Stability of Power Grids using Graph Neural Networks
We investigate the feasibility of applying graph neural networks (GNN) to predict dynamic stability of synchronisation in complex power grids using the single-node basin stability (SNBS) as a measure.
Statistical Analysis of Tipping Pathways in Agent-Based Models
Agent-based models are a natural choice for modeling complex social systems.
Dimensionality Reduction
Physics and Society
Anticipation-induced social tipping -- Can the environment be stabilised by social dynamics?
Acknowledging that societies have the additional capability for foresight, this work proposes a conceptual feedback model of socio-ecological co-evolution with the specific construct of anticipation acting as a mediator between the social and natural system.
Physics and Society
Multi-task GANs for Semantic Segmentation and Depth Completion with Cycle Consistency
Semantic segmentation and depth completion are two challenging tasks in scene understanding, and they are widely used in robotics and autonomous driving.
When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
Then, we further review the performance of RL and meta-learning from the aspects of accuracy or transferability or both of them in autonomous systems, involving pedestrian tracking, robot navigation and robotic manipulation.
Delay master stability of inertial oscillator networks
Time lags occur in a vast range of real-world dynamical systems due to finite reaction times or propagation speeds.
Dynamical Systems Adaptation and Self-Organizing Systems
Machine Discovery of Partial Differential Equations from Spatiotemporal Data
The study presents a general framework for discovering underlying Partial Differential Equations (PDEs) using measured spatiotemporal data.
Deterministic limit of temporal difference reinforcement learning for stochastic games
Reinforcement learning in multi-agent systems has been studied in the fields of economic game theory, artificial intelligence and statistical physics by developing an analytical understanding of the learning dynamics (often in relation to the replicator dynamics of evolutionary game theory).
Multiagent Systems
Operationalization of Topology of Sustainable Management to Estimate Qualitatively Different Regions in State Space
To apply the framework of Topology of Sustainable Management (TSM) by Heitzig et al. (2016) to dynamical models, we connect it to viability theory (VT) via a variant definition of the former.
Optimization and Control
Deep Graphs - a general framework to represent and analyze heterogeneous complex systems across scales
These include, most importantly, an explicit association of information with possibly heterogeneous types of objects and relations, and a conclusive representation of the properties of groups of nodes as well as the interactions between such groups on different scales.
Data Analysis, Statistics and Probability Social and Information Networks Atmospheric and Oceanic Physics Physics and Society
Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package
Additionally, \texttt{pyunicorn} provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis (RQA), recurrence networks, visibility graphs and construction of surrogate time series.
Data Analysis, Statistics and Probability Atmospheric and Oceanic Physics
Optimal model-free prediction from multivariate time series
Here a prediction scheme that overcomes this strong limitation is introduced utilizing a causal pre-selection step which drastically reduces the number of possible predictors to the most predictive set of causal drivers making a globally optimal search scheme tractable.
Macroscopic description of complex adaptive networks co-evolving with dynamic node states
In many real-world complex systems, the time-evolution of the network's structure and the dynamic state of its nodes are closely entangled.
Physics and Society
