Part of solution verification consists in comparing computed output with analytic solutions (so called "benchmark solutions").
Other indirect techniques are available: the most important of which is probably checking to see whether and at what rate computed output converges to a stable solution as the time and spatial resolution of the discretization grid gets finer.
The vanilla backpropagation [29] algorithm was used to minimize the differences between the computed output and the target value.
On that basis, according to McKinsey, the total cost of the data center functions would be $366 a month per unit of computing output, compared with $150 a month for the conventional data center.
To compute output error performance objectives, the EBB equation is discretized via finite differences in space and time and reformulated to a state space system.
In the present work a systematic methodology for computing output stabilizing feedback control laws for nonlinear systems subject to saturating inputs is presented.
The proposed results are motivated from its use for designing minimal-order observers and for computing output feedback control laws by a particular technique.
In the second step, we compute output functionals of the solution, such as the Radar Cross Section (RCS), independently of the dimension of the discretization space, for many different parameter values in a many-query context at very little cost.
This is moving into the realm of full size computing output, while still maintaining a small amount of power usage.
Thus in a conventional butterfly, to compute output X, two floating point adders/subtractors are required.
