Challenges of evolvable hardware: past, present and the path to a promising future
Genetic Programming and Evolvable Machines
A cellular automata based approach for prediction of hot mudflow disaster area
ICCSA'10 Proceedings of the 2010 international conference on Computational Science and Its Applications - Volume Part II
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Fluid flow in porous media is a dynamic process that is traditionally modeled using PDE (Partial Differential Equations). In this approach, physical properties related to fluid flow are inferred from rock sample data. However, due to the limitations posed in the sample data (sparseness and noise), this method often yields inaccurate results. Consequently, production information is normally used to improve the accuracy of property estimation. This style ofmodeling is equivalent to solving inverse problems.We propose using a Genetic Algorithm (GA) as an inverse method to model fluid flow in a pore network Cellular Automaton (CA). This GA evolves the CA to produce specified flow dynamic responses. We apply this method to a rock sample data set. The results are presented and discussed. Additionally, the prospect of building the pore network CA machine is discussed.