Abstract: Systems and methods for cooling a datacenter are disclosed. In at least one embodiment, a number of multi-dimensional column-based heat dissipation features enable cooling by a cooling media flowing there through so that an individual heat dissipation column having a first dimension and a second dimension may be supported, with the first dimension being normal relative to an axial flow path of the cooling media, with the second dimension being parallel or offset from parallel relative to the axial flow path and with the second dimension being more than the first dimension.

Abstract: To assist a machine learning environment in modelling a complex physical simulation (such as a numerical simulation or physics simulation), a correlation between input coordinates is determined. For example, a discrete solution (e.g., the correlation between the plurality of input coordinates) may be obtained from a non-discrete (e.g., continuous) physics space by performing a conversion from the physics space to a grid space. This correlation is input along with the coordinates into a machine learning environment to obtain results from the simulation. As a result, instead of implementing resource and power-intensive simulations to solve these computation problems, a machine learning environment implemented using less power and computing resources may solve these computation problems in a faster and more efficient manner.