Covariance-based smoothed particle hydrodynamics. A machine-learning application to simulating disc fragmentation
This work addresses disc fragmentation simulations in astrophysics, offering an incremental improvement by integrating machine learning into SPH methods.
The authors tackled the problem of simulating disc fragmentation in astrophysics by developing a PCA-based machine learning version of smoothed particle hydrodynamics (SPH) that uses anisotropic smoothing ellipsoids. The result showed that protostar formation in disc fragmentation was much more persistent and abundant in the anisotropic simulation compared to the isotropic case.
A PCA-based, machine learning version of the SPH method is proposed. In the present scheme, the smoothing tensor is computed to have their eigenvalues proportional to the covariance's principal components, using a modified octree data structure, which allows the fast estimation of the anisotropic self-regulating kNN. Each SPH particle is the center of such an optimal kNN cluster, i.e., the one whose covariance tensor allows the find of the kNN cluster itself according to the Mahalanobis metric. Such machine learning constitutes a fixed point problem. The definitive (self-regulating) kNN cluster defines the smoothing volume, or properly saying, the smoothing ellipsoid, required to perform the anisotropic interpolation. Thus, the smoothing kernel has an ellipsoidal profile, which changes how the kernel gradients are computed. As an application, it was performed the simulation of collapse and fragmentation of a non-magnetic, rotating gaseous sphere. An interesting outcome was the formation of protostars in the disc fragmentation, shown to be much more persistent and much more abundant in the anisotropic simulation than in the isotropic case.