Learning Reduced Order Dynamics via Geometric Representations
Imran Nasim, Melanie Weber
SCML 2024
This paper examines the computational aspects of the reconfigurable network model. The computational power of the model is investigated under several network topologies and with several variants of the model assumed. In particular, it is shown that there are reconfigurable machines based on simple network topologies that are capable of solving large classes of problems in constant time. These classes depend on the kinds of switches assumed for the network nodes. Reconfigurable networks are also compared with various other models of parallel computation, like PRAMS and Branching Programs. © 1991.
Imran Nasim, Melanie Weber
SCML 2024
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