Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Evolutionary games on graphs

Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first three sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fourth section surveys the topological complications implied by non-mean-field-type social network structures in general. The last three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock-Scissors-Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.Comment: Review, final version, 133 pages, 65 figure

Subject and Aesthetic Interface - an inquiry into transformed subjectivities

The present PhD-thesis seeks new definitions of human subjectivity in an age of technoscience and a networked, globalized, Information Society. The perspective presented relates to Philosophy of Science, which includes the Human, the Natural, the Social and the Life Sciences. The project is directed at addressing, and aims to participate in, the further development of Philosophy of Science, or rather, the philosophy of knowing, which leaves a perspective broader than that of science. Methodologically, I combine readings of technoetic artworks, which I approach from a hermeneutical-semiotic perspective, with transdisciplinary research into existing theory concerning the human subject. These readings form my case studies. I keep a particular focus on holistic biophysics (Mae Wan Ho, James Oschman, Marko Bischof). Furthermore, Søren Brier's cybersemiotic theory of communication, cognition and consciousness, which combines a cybernetic-autopoietic and a Peircean semiotic perspective, plays a central role in the project. The project has three parts. Part one contextualizes the study within philosophy of science. It discusses relevant epistemologies, and places the case studies in an art categorical context. It further discusses the philosophical problems involved in writing an academic thesis in the form of a linear, argumentative, critical style, and how it affects the process of meaning making in a way that has consequences to my research. The second part consists of four case studies, each under an overall theme, which applies to the question of human subjectivity. Here I build the concept Extended Sentience, and the concept of an Ideal User. The Ideal User functions as a conceptual frame, which allows me to gradually add more elements to a theory of an altered human subject and knower. The third part presents new ontologies under three basic themes: Time and Relativity, The Life Cycles of Metaphors, and Logos Philosophy and Virtual Grids. These ontologies strongly affect ways of interpretation made in part one and two. Part Three allows more space to my subjective thought processes, which will take precedence over the literature applied. Thus, I, as a post-objective subject observer, will become more transparent. Finally, I will seek an overall conclusion to the project, which should clarify areas where it is evident that the human subject must be reconsidered at a pre-scientific level. It is my thesis that the foundation for human knowledge generation is changing drastically today, and that it has become crucial to reconsider a common understanding of what constitutes the human knower

Hybrid Societies : Challenges and Perspectives in the Design of Collective Behavior in Self-organizing Systems

Hybrid societies are self-organizing, collective systems, which are composed of different components, for example, natural and artificial parts (bio-hybrid) or human beings interacting with and through technical systems (socio-technical). Many different disciplines investigate methods and systems closely related to the design of hybrid societies. A stronger collaboration between these disciplines could allow for re-use of methods and create significant synergies. We identify three main areas of challenges in the design of self-organizing hybrid societies. First, we identify the formalization challenge. There is an urgent need for a generic model that allows a description and comparison of collective hybrid societies. Second, we identify the system design challenge. Starting from the formal specification of the system, we need to develop an integrated design process. Third, we identify the challenge of interdisciplinarity. Current research on self-organizing hybrid societies stretches over many different fields and hence requires the re-use and synthesis of methods at intersections between disciplines. We then conclude by presenting our perspective for future approaches with high potential in this area

Dynamical modeling of collective behavior from pigeon flight data: flock cohesion and dispersion

Several models of flocking have been promoted based on simulations with qualitatively naturalistic behavior. In this paper we provide the first direct application of computational modeling methods to infer flocking behavior from experimental field data. We show that this approach is able to infer general rules for interaction, or lack of interaction, among members of a flock or, more generally, any community. Using experimental field measurements of homing pigeons in flight we demonstrate the existence of a basic distance dependent attraction/repulsion relationship and show that this rule is sufficient to explain collective behavior observed in nature. Positional data of individuals over time are used as input data to a computational algorithm capable of building complex nonlinear functions that can represent the system behavior. Topological nearest neighbor interactions are considered to characterize the components within this model. The efficacy of this method is demonstrated with simulated noisy data generated from the classical (two dimensional) Vicsek model. When applied to experimental data from homing pigeon flights we show that the more complex three dimensional models are capable of predicting and simulating trajectories, as well as exhibiting realistic collective dynamics. The simulations of the reconstructed models are used to extract properties of the collective behavior in pigeons, and how it is affected by changing the initial conditions of the system. Our results demonstrate that this approach may be applied to construct models capable of simulating trajectories and collective dynamics using experimental field measurements of herd movement. From these models, the behavior of the individual agents (animals) may be inferred