Covariance-based smoothed particle hydrodynamics. A machine-learning application to simulating disc fragmentation

TL;DR

This paper introduces a PCA-based machine learning extension to the SPH method, enabling anisotropic smoothing for simulating disc fragmentation, resulting in more persistent and abundant protostar formation.

Contribution

It presents a novel PCA-based, anisotropic SPH method using covariance tensors and Mahalanobis metric for improved simulation of disc fragmentation.

Findings

More persistent protostar formation in anisotropic simulations

Increased abundance of protostars compared to isotropic case

Enhanced stability of disc fragmentation results

Abstract

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.