Marcelino Quito

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We show a new approach for solving the N-body problems based on neural networks. Without loss of generality, we derived a network solution for the time-dependent positions of N bodies in self-gravitating systems. The simulation is limited to a system of collisionless disks-a case for determining the spatial distributions of dark matter and in reproducing(More)
We employ an agent-based model to show that memory and the absence of an a priori best strategy are sufficient for self-segregation and clustering to emerge in a complex adaptive system with discrete agents that do not compete over a limited resource nor contend in a winner-take-all scenario. An agent starts from a corner of a two-dimensional lattice and(More)
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