Physics of Self-Organization in Complex Systems

The role of fundamental physics principles, information and entropy in the change of complexity


One Day Satellite Meeting, September 2018

at CCS18, 23-28 Sept , Thessaloniki, Greece

Accepted abstracts will be invited to publish in a proceedings volume with SpringerNature

Confirmed Keynote Speakers:

Prof. Dr. Mile Gu, The Quantum and Complexity Science Initiative, Nanyang Technological Unversity, Singapore. Tentative title: "Quantum Simplicity - a tour of complexity and structure from the perspective of quantum observers."

Prof. Dr. Stefan Thurner, Section for Science of Complex Systems, Medical University of Vienna. Tentative title: Driven non-equilibrium systems as sample space reducing processes: How statistics emerges as an interplay of driving and relaxing.

Prof. Dr. Henrik Jeldtoft Jensen, Mathematical Physics and Leader of the Centre for Complexity Science, Imperial College London. "Co-evolving individuals and the emergence of adaptive structures: group selection versus the individual. "

Prof. Dr. Murilo Pereira de Almeida, Departamento de Fisica, Universidade Federal do Ceará, Brazil, "Features of turbulence in the intermediate range"

Confirmed invited speakers:

1. Prof. Dr. Ashwin Vaidya, Department of Mathematical Sciences; Director, Science Honors Innovation Program; College of Science and Mathematics; Montclair State University; Montclair, NJ 07043 “Self-Organization in Fluid-Solid Interacting Systems Far from Thermodynamic Equilibrium”

2. Prof. Dr. Giridhar Nandikotkur, Physics Department, Fairleigh Dickinson University, Teaneck NJ “Investigating Inherent Timescales of Variability in Blazers Using Structure Functions”

Also Contributed Talks and Posters (see below)

Scope:

The “Physics of Self-Organization in Complex Systems” (PSOCS) brings together researchers who use fundamental physics principles to explore the phenomenon of self-organization in complex systems of any nature. This includes the flow of free energy, the Principle of Least Action, Lagrangian and Hamiltonian approaches and others. The concepts of quantum mechanics, information and entropy are very much on the forefront of this research. In the tradition of Ilya Prigogine and Herman Haken, we want to illuminate the core principles of self-organization and to apply them to as many systems as possible. Most important are observations of universal principles and phenomena valid across wide range of complex systems, from physical, to chemical, biological, economical, technological and social, and across all scales, from subatomic to the scales of the universe. In recent years, there are many advances in the theoretical, computational and simulation approaches to study out-of-equilibrium thermodynamic systems, and the patterns of temporal and spatial structure emerging as a result of energy flows, that can be presented.

Carl Sagan has observed an acceleration in emergence in structures in the universe in his Cosmic Calendar from the Big Bang until today. Eric Chaisson in his work on Cosmic Evolution has provided an explanation of this accelerated increase of organization in non-equilibrium systems in the Universe. According to the data, this acceleration is correlated to increased free energy rate density and the work that it does to structure systems for the emergence of order, information, computation and other complex systems characteristics.

The phenomena in complex systems are statistical and probabilistic by nature that is why we need the application of quantum computation in order describe them, something that may be impossible or almost impossible by classical computation. Anyone who works on quantum computation and can connect it to describing and solving probabilistic situations in complex systems will advance the field complexity science greatly. Complex systems by definition contain a lot of information, even at the very basic physical level - on atomic and molecular scale, and big data approaches are necessary for their description. Deep Learning and other Artificial Intelligence algorithms will be very appropriate to help solve some of the biggest problems in complex systems science, such as the description and prediction of emergence and structure formation.

Some sample open questions in this field are: Are there attractors of any kind that drive self-organization to higher levels? How to measure those levels of organization? What is the correlation, causation and interaction between different characteristics of complex systems, such as their structure, size, free energy rate density, number of agents, rates of energy and mass flows and any other characteristic and how do they produce the variety that we see around us? Are there positive and negative feedback loops between those characteristics and how do they influence the rates of self-organization? What are the growth equations for self-organization in systems of different nature? Transport (flow) networks for energy and matter seem to be the most ubiquitous characteristic for non-equilibrium, self-organizing systems. What are the best methods to characterize those networks? Is there a limit for self-organization or it can continue infinitely? What is common in self-organization at different scales, from the quantum to the galactic?

In short, we seek to advance conceptual, computational, modelling and mathematical models and empirical applications of the physical aspects of complex systems of any nature and non-equilibrium thermodynamics, as correlated to the increase free energy rate density, emergence, information, computation, structure, functionality and efficiency.

Anyone who works on those questions is welcome to submit an abstract to this session.

Paper Topics (partial list):

Entropy, Information, Least Action Principle, Variational Principles, Mathematical Models, Self-organization, Out-of-equilibrium Systems, Non-equilibrium Thermodynamics, Non-linear processes, Chaos, Catastrophe Theory, Free energy Rate Density, Cosmic Evolution, Positive and Negative feedback loops, Scaling Laws, Complexity, Quantum Computation, Artificial Intelligence.

Goals:

● Promote discussion and research on fundamental physics approaches to understanding the physical description and principles, attractors, free energy and driving forces behind self-organization in complex systems, explored from the intersecting perspectives of nonlinear dynamics, information, entropy, positive and negative feedback loops, computation and others.

● Publish proceedings of the event in Springer Proceedings in Complexity.

Abstracts or papers are to be submitted to EasyChair. https://easychair.org/cfp/CCS18PhysicsofSelfOrganization If you have any difficulties, you can submit also by email. Please, if you are planing to submit and abstract, notify us of that intent, now. Send and email to ggeorgiev AT WPI.edu. Thank you!

Important dates:

Abstract Deadline: 15th June

Notifications of Acceptance: Rolling (to 10th July)

Paper Submission Deadline: 30th November

Schedule:

Morning Session

9:00-9:15 | Opening

9:15-9:55 | Keynote 1

Prof. Dr. Stefan Thurner, Section for Science of Complex Systems, Medical University of Vienna. Title: Driven non-equilibrium systems as sample space reducing processes: How statistics emerges as an interplay of driving and relaxing.

9:55-10:10 | Tianqi Chen, with Gayatri Phadke, Jennifer Satterwhite-Warden, Dilip Kondepudi, James Dixon and James Rusling: University of Connecticut, “Collective Self-Motion of Multiple Benzoquinone Particles at the Air-Water Interface”

10:10-10:25 | Benjamin De Bari, with James Dixon, Bruce Kay and Dilip Kondepudi, University of Connecticut, “Physical Predictions from Dynamical Systems Modeling of an Electrodynamic Dissipative Structure”

10:30-11:00 | Coffee Break | 30 min

11:00-11:20 | Georgi Georgiev, Ph.D., Worcester Polytechnic Inst. & Assumption College, MA, USA, Title: "Structure formation by flows of free energy and increased energy throughput through those structures: Internal entropy minimization and external entropy production "

11:20-11:50 | Invited 1

Prof. Dr. Ashwin Vaidya, Department of Mathematical Sciences; Director, Science Honors Innovation Program; College of Science and Mathematics; Montclair State University; Montclair, NJ 07043 “Self-Organization in Fluid-Solid Interacting Systems Far from Thermodynamic Equilibrium”

11:50-12:00 | Ram Poudel, UMass Amherst, "Dynamics of open and evolving system: Human Society."

12:00-12:40 | Keynote 2

Prof. Dr. Henrik Jeldtoft Jensen with Katharina Brinck, Mathematical Physics and Leader of the Centre for Complexity Science, Imperial College London. "Co-evolving individuals and the emergence of adaptive structures: group selection versus the individual. "

12:40-1:00 | Roundtable Discussion - Morning Speakers


1:00-2:30 | Lunch and Poster Session | 90 min


Afternoon Session - (2:30pm - 6:30pm)

2:30-3:10 | Keynote 3

Prof. Dr. Mile Gu, The Quantum and Complexity Science Initiative, Nanyang Technological Unversity, Singapore. Title: "Quantum Simplicity - a tour of complexity and structure from the perspective of quantum observers."

3:10-3:25 |

3:25-3:40 |

3:40-3:55 | Travis Butler, Assumption College, "Stellar self-organization"

4:00-4:30 | Coffee Break

4:30-5:00 | Invited 2

Prof. Dr. Giridhar Nandikotkur, Physics Department, Fairleigh Dickinson University, Teaneck NJ “Exploring Long-term Memory in Time Series of X-ray Radiation from Active Nuclei of Galaxies”.

5:00-5:15 | P. Adrian Frazier: University of Connecticut, "How do variational principles get into goal directed behavior?"

5:15-5:30 |

5:30-6:10 | Keynote 4

Prof. Dr. Murilo Pereira de Almeida, Departamento de Fisica, Universidade Federal do Ceará, Brazil, "Features of turbulence in the intermediate range"

6:10-6:25 | Roundtable Discussion - Afternoon Speakers

6:25-6:30 | Closing

Evening Social Event

Presenter Abstracts

(PDF Download).

Organizer:

Georgi Georgiev, Ph.D., Worcester Polytechnic Inst. & Assumption College, MA, USA


Abstracts:

Co-evolving individuals and the emergence of adaptive structures: group selection versus the individual.

Henrik Jeldtoft Jensen1,2, Katharina Brinck1

(1) Centre for Complexity Science and Department of Mathematics, Imperial College London, South Kensington Campus, SW7 2AZ, UK; h.jensen@imperial.ac.uk

(2) Institute of Innovative Research, Tokyo Institute of Technology, 4259, Nagatsuta-cho, Yokohama 226-8502, Japan.

Ecosystems, whether biological or economical, say, are the emergent products of evolutionary and adaptive dynamics of a community of interacting agents. Through a combination of adaptive pressure arising from the external environmental and the co-adaptation within the community, agents evolve as a result of selection and networks of interdependence are produced. As community structures are formed, one may ask about the relative importance of selection of the individual compared to selection of the communities. This is the long-standing debate amongst evolutionary biologist concerning group selection versus individual selection. Without an appropriate quantitative modelling framework, the relative importance of bottom-up and top-down control is very difficult to phrase in a way sufficiently precise and transparent to allow one to determine if the selection of individuals according to their phenotype is more important than the selection acting at collective structures, i.e. group level. We present a way of quantifying the relative weight of natural selection and coadaptation grounded in information theory. We assess the relative role of bottom-up and top-down control in the evolution of ecological systems and analyse the information transfer in an individual based stochastic complex systems model, the Tangled Nature Model of evolutionary ecology. As coadaptation progresses, we show that ecological communities evolve from mainly bottom-up controlled early-successional systems to more strongly top-down controlled late-successional systems. Agents which have a high influence on selection transfer are also generally more abundant. Hence our findings imply that ecological communities are shaped by a dialogue of bottom-up and top-down control, where the role of the systemic selection and integrity becomes more pronounced the further the ecosystem is developed.

Acknowledgements

Simulations have been run on the High Performance Cluster by the Imperial College Computing Service whom we thank sincerely for providing these facilities. KB thanks Imperial Colleges Department of Mathematics for funding her PhD work.

References

[1] K. Brinck and H.J. Jensen, The evolution of ecosystem ascendency in a complex systems based model. J. Theo. Biol.. 428, 18-25 (2017)

[2] K. Brinck and H.J. Jensen, Bottom-up versus top-down control and the transfer of information complex model ecosystems. (Brinck’s PhD thesis and manuscript in preparation).

Thermodynamics of Self-Organization in Fluid-Solid Interacting Systems

Ashwin Vaidya

Department of Mathematical Sciences, Montclair State University, Montclair, NJ.

Email: vaidyaa@montclair.edu

The interaction of fluids with solids is an age old problem and has given rise to several interesting mathematical and physical problems on pattern formation, stability and bifurcations. Pattern formation is seen to be a consequence of thermodynamic disequilibrium in the system and lends itself to mechanical and thermodynamic arguments, among which is the well known principle of Maximum Entropy Production (MEP). This theory has proven to be effective in certain contexts although its overarching effectiveness as a universal principle and connections to several other variational principles in physics remain to be established. The terminal orientation of a rigid body in a fluid is a relatively simple example of a dissipative system out of thermodynamic equilibrium and serves as a perfect testing ground for the validity of the MEP principle.

A body interacting with fluid generates flow around it resulting in dissipative losses. Typically, dynamical equations have been employed in deriving the equilibrium states of such immersed bodies in fluids, but they are far too complex and become analytically intractable when inertial effects come into play. At that stage, our only recourse is to rely on numerical techniques which can be computationally heavy and time consuming.

Our previous work [1,2] has revealed that the MEP principle is a reliable tool to help predict the equilibrium orientation of highly symmetric bodies such as cylinders and spheroids, near thermodynamic equilibrium. In recent work [3], we expand our analysis to examine bodies with lesser symmetry (for instance, a half-cylinder in a flow) and Reynolds numbers (inertial parameter) substantially greater than zero, which render the problem far from thermodynamic equilibrium. Experiments and numerical studies indicate that symmetry-breaking and inertia have a nuanced effect on the MEP principle, giving rise to interesting variations from MEP. This talk will focus on some of the results described above. Extensions of this study to other types of pattern selection and fluid structure interaction problems will also be discussed.

References

1. B.J.Chung and A. Vaidya, An optimal principle in fluid-structure interaction, Physica D, 237( 22), 2945-2951, 2008.

2. B.J. Chung, McDermid, K. and A. Vaidya, On the affordances of the MaxEP principle, European Physical Journal B: Condensed Matter and Complex Systems, 87, 2014.

3. B. Chung, B. Ortega, A. Vaidya, Entropy Production in a Fluid-Solid System Far From Thermodynamic Equilibrium, European Physical Journal E: Soft Matter and Biological Physics, 40: 105, 2017.