Cosmoboffins use neural networks to build dark matter maps the easy way
https://www.theregister.co.uk/2019/05/20/neural_networks_dark_matter/
Spinning up dark matter simulations is computationally expensive so a team of cosmologists are turning to AI models instead.
Generative adversarial networks or GANs are good at learning patterns from data and reproducing them in new samples. In this case, the team led by researchers from the Lawrence Berkeley National Laboratory used weak gravitational lensing maps as input to simulate more of the same images as output.
Peak your interest for answers to this dark matter conundrum by reviewing an alternative theory of the universe from a more 4th dimensional perspective. This concept was previously approached in the book, 'The Evolutioning of Creation: Volume 2', copyrighted in 2011. As the predominant condition of the universe is a combination of dark energy and dark matter (i.e., massless matter or negative mass density), then the existence of our universe [or positive mass density] is more of an intrusion upon this norm. Such is it that positive density mass (i.e., baryonic mass) provides for a displacement effect, which is expressed as if the positive mass has intruded upon the inertial condition of the space-time continuum.
You can peak your interest for answers to this mystery from the perspective of the science fiction novel, 'Shadow-Forge Revelations'. As science fiction imitates science theory, this novel discusses how dark energy and dark matter interplays with the concept of the expanding universe from the inception of its creation.