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 definitely 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 of medical . Families 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 emerging and structure formation. 

 Some sample open questions in this field are: Are there attractives 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 which 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.