Spatial organization and evolutional period of the epidemic model using cellular automata

We investigate epidemic models with spatial structure based on the cellular automata method. The construction of the cellular automata is from the study by Weimar and Boon about the reaction-diffusion equations [Phys. Rev. E 49, 1749 (1994)]. Our results show that the spatial epidemic models exhibit the spontaneous formation of irregular spiral waves at large scales within the domain of chaos. Moreover, the irregular spiral waves grow stably. The system also shows a spatial period-2 structure at one dimension outside the domain of chaos. It is interesting that the spatial period-2 structure will break and transform into a spatial synchronous configuration in the domain of chaos. Our results confirm that populations embed and disperse more stably in space than they do in nonspatial counterparts.Comment: 6 papges,5 figures. published in Physics Review

A multiscale modeling approach for the progressive failure analysis of textile composites

Ph.DDOCTOR OF PHILOSOPH

Foundry sand reclamation by the gas-contact process

ABSTRACT: Context and motivation -- Objectives -- Organization of the thesis -- Review of the thermal methods of foundry sand reclamation -- Thermal reclamation of foundry sand -- Comparison of the thermal methods of foundry sand reclamation -- Literature review -- Gas-solid vertical flow regimes -- Particle dynamics and dilute fluidized solid flow -- Heat transfer in gas-solid flow -- Particle combustion -- Modeling of the two-phase flow system -- Theoretical approach -- Theoretical analysis -- Heat transfer rate limitation models -- Heat transfer rate and resing combustion process limitation models -- Experimental installation and data acquisition -- Gas-solid installation -- Gas flow measurements -- Solid flow measurement -- Temperature measurement -- Measurement of resin conversion -- Physical properties of gas and solid -- Experimental results, discussion and comparison with modeling results -- Experimental results -- Comparison of experimental and modeling results

Learning Agent Communication under Limited Bandwidth by Message Pruning

Communication is a crucial factor for the big multi-agent world to stay organized and productive. Recently, Deep Reinforcement Learning (DRL) has been applied to learn the communication strategy and the control policy for multiple agents. However, the practical \emph{\textbf{limited bandwidth}} in multi-agent communication has been largely ignored by the existing DRL methods. Specifically, many methods keep sending messages incessantly, which consumes too much bandwidth. As a result, they are inapplicable to multi-agent systems with limited bandwidth. To handle this problem, we propose a gating mechanism to adaptively prune less beneficial messages. We evaluate the gating mechanism on several tasks. Experiments demonstrate that it can prune a lot of messages with little impact on performance. In fact, the performance may be greatly improved by pruning redundant messages. Moreover, the proposed gating mechanism is applicable to several previous methods, equipping them the ability to address bandwidth restricted settings.Comment: accepted as a regular paper with poster presentation @ AAAI20. arXiv admin note: text overlap with arXiv:1903.0556