Pattern recognition issues on anisotropic smoothed particle hydrodynamics
This is an incremental theoretical discussion linking SPH to pattern recognition and AI, potentially benefiting computational astrophysics.
The paper identifies smoothed particle hydrodynamics (SPH) as an unsupervised machine learning case when including anisotropy detection for shock resolution, and suggests AI could treat SPH particles as collaborative agents to build a knowledge base for astrophysical simulations like star formation.
This is a preliminary theoretical discussion on the computational requirements of the state of the art smoothed particle hydrodynamics (SPH) from the optics of pattern recognition and artificial intelligence. It is pointed out in the present paper that, when including anisotropy detection to improve resolution on shock layer, SPH is a very peculiar case of unsupervised machine learning. On the other hand, the free particle nature of SPH opens an opportunity for artificial intelligence to study particles as agents acting in a collaborative framework in which the timed outcomes of a fluid simulation forms a large knowledge base, which might be very attractive in computational astrophysics phenomenological problems like self-propagating star formation.