Thomas Stemler
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View article: Modelling collective motion with simple selfish herd domain optimisation rules
Modelling collective motion with simple selfish herd domain optimisation rules Open
One of the most prominent explanations for aggregate behaviour in animal groups is known as the selfish herd hypothesis. The selfish herd hypothesis proposes that each agent has a “domain of danger” whose area is proportional to the risk o…
View article: Improving forecasts of imperfect models using piecewise stochastic processes
Improving forecasts of imperfect models using piecewise stochastic processes Open
Forecasting complex systems is important for understanding and predicting phenomena. Due to the complexity and error sensitivity inherent in these predictive models, forecasting proves challenging. This paper presents a novel approach to a…
View article: A Selfish Herd with a Target
A Selfish Herd with a Target Open
One of the most striking phenomena in biological systems is the tendency for biological agents to spatially aggregate, and subsequently display further collective behaviours such as rotational motion. One prominent explanation for why agen…
View article: Modeling Uncertainties for Automated and Connected Vehicles in Mixed Traffic
Modeling Uncertainties for Automated and Connected Vehicles in Mixed Traffic Open
The advent of automated vehicles (AVs) and connected automated vehicles (CAVs) creates significant uncertainties in infrastructure planning due to many unknowns, such as performance variability and user adaptation. As technologies are stil…
View article: Model Calibration and Validation From A Statistical Inference Perspective
Model Calibration and Validation From A Statistical Inference Perspective Open
Despite the general consensus in transport research community that model calibration and validation are necessary to enhance model predictive performance, there exist significant inconsistencies in the literature. This is primarily due to …
View article: A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks
A Backpropagation Algorithm for Inferring Disentagled Nodal Dynamics and Connectivity Structure of Dynamical Networks Open
Dynamical networks are versatile models that describe a variety of behaviours such as synchronisation and feedback in networks of coupled dynamical components. However, applying these models in real systems is difficult as prior informatio…
View article: Network representations of attractors for change point detection
Network representations of attractors for change point detection Open
A common approach of monitoring the status of physical and biological systems is through the regular measurement of various system parameters. Changes in a system's underlying dynamics manifest as changes in the behaviour of the observed t…
View article: Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations
Exploring Model Misspecification in Statistical Finite Elements via Shallow Water Equations Open
The abundance of observed data in recent years has increased the number of statistical augmentations to complex models across science and engineering. By augmentation we mean coherent statistical methods that incorporate measurements upon …
View article: Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology Open
Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number o…
View article: Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology
Selecting embedding delays: An overview of embedding techniques and a new method using persistent homology Open
Delay embedding methods are a staple tool in the field of time series analysis and prediction. However, the selection of embedding parameters can have a big impact on the resulting analysis. This has led to the creation of a large number o…
View article: Modelling Uncertainties for Automated and Connected Vehicles in Mixed Traffic
Modelling Uncertainties for Automated and Connected Vehicles in Mixed Traffic Open
The disruptive nature of automated and connected vehicles (AVs and CAVs) poses increasing risks to infrastructure planning. Predicting their exact impact is impossible because of many unknowns. We address these uncertainties by establishin…
View article: Multi-objective graph partitioning for the MFD-based perimeter control of an urban network
Multi-objective graph partitioning for the MFD-based perimeter control of an urban network Open
The Macroscopic Fundamental Diagrams (MFDs) can be applied to guide perimeter control strategies where the network is divided into several manageable regions. Although there has been a large body of literature on the MFD theory, more resea…
View article: Backpropagation on Dynamical Networks
Backpropagation on Dynamical Networks Open
Dynamical networks are versatile models that can describe a variety of behaviours such as synchronisation and feedback. However, applying these models in real world contexts is difficult as prior information pertaining to the connectivity …
View article: Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan
Multiple Sensors Data Integration for Traffic Incident Detection Using the Quadrant Scan Open
Non-recurrent congestion disrupts normal traffic operations and lowers travel time (TT) reliability, which leads to many negative consequences such as difficulties in trip planning, missed appointments, loss in productivity, and driver fru…
View article: Statistical finite elements for misspecified models
Statistical finite elements for misspecified models Open
Significance Science and engineering have benefited greatly from the ability of finite element methods (FEMs) to simulate nonlinear, time-dependent complex systems. The recent advent of extensive data collection from such complex systems n…
View article: Markov modeling via ordinal partitions: An alternative paradigm for network-based time-series analysis
Markov modeling via ordinal partitions: An alternative paradigm for network-based time-series analysis Open
Mapping time series to complex networks to analyze observables has recently become popular, both at the theoretical and the practitioner's level. The intent is to use network metrics to characterize the dynamics of the underlying system. A…
View article: Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information
Optimal Shadowing Filter for a Positioning and Tracking Methodology with Limited Information Open
Positioning and tracking a moving target from limited positional information is a frequently-encountered problem. For given noisy observations of the target’s position, one wants to estimate the true trajectory and reconstruct the full pha…
View article: Multiscale ordinal network analysis of human cardiac dynamics
Multiscale ordinal network analysis of human cardiac dynamics Open
In this study, we propose a new information theoretic measure to quantify the complexity of biological systems based on time-series data. We demonstrate the potential of our method using two distinct applications to human cardiac dynamics.…