Computer Science – Artificial Intelligence
Scientific paper
Jan 1993
adsabs.harvard.edu/cgi-bin/nph-data_query?bibcode=1993egte.conf..350c&link_type=abstract
In NASA. Ames Research Center, The Evolution of Galaxies and Their Environment p 350-351 (SEE N93-26706 10-90)
Computer Science
Artificial Intelligence
Artificial Intelligence, Astronomical Models, Automata Theory, Galactic Evolution, Neural Nets, Star Formation, Star Formation Rate, Explosions, Gas Density, Hydrodynamics, Hydrogen, Velocity Distribution
Scientific paper
Two ideas appear frequently in theories of star formation and galaxy evolution: (1) star formation is nonlocally excitatory, stimulating star formation in neighboring regions by propagation of a dense fragmenting shell or the compression of preexisting clouds; and (2) star formation is nonlocally inhibitory, making H2 regions and explosions which can create low-density and/or high temperature regions and increase the macroscopic velocity dispersion of the cloudy gas. Since it is not possible, given the present state of hydrodynamic modeling, to estimate whether one of these effects greatly dominates the other, it is of interest to investigate the predicted spatial pattern of star formation and its temporal behavior in simple models which incorporate both effects in a controlled manner. The present work presents preliminary results of such a study which is based on lattice galaxy models with various types of nonlocal inhibitory and excitatory couplings of the local SFR to the gas density, temperature, and velocity field meant to model a number of theoretical suggestions.
Chappell David
Scalo John
No associations
LandOfFree
Automata network models of galaxy evolution does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with Automata network models of galaxy evolution, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and Automata network models of galaxy evolution will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-1260347