The trials and tribulations of uncertainty run deep in the hearts of experimentalists. From our biased human minds, to the imprecision of our measurement devices, to the quantum uncertainty of our universe, errors have confounded scientists for eons. Even within the safe and deterministic world of computation, inexactness is rampant.

There are two main types of numerical errors. The first of which is result of computers having a limited number of bits (32 or 64) to represent numbers. All the coolest real numbers have an infinite number of digits so we’re restricted to representing numbers that differ by machine epsilon.

The second source of numerical error pops up a lot in numerical algorithms, including those implemented in Step for solving differential equations. It would be nice if we could perform exact symbolic computation all the time, but physics can get messy, especially with errors! Almost any time we take a derivative on computer, we approximate it using a taylor series. This approximation works great if we keep an infinite number of terms but who has the time to calculate all that? Thus, our results differ from the exact answer based on where we truncate our taylor series. This truncation error combined with the floating point error mentioned above, has the potential to cause serious numerical instabilities and pain for computational scientists.

Numerical issues aside*, in true experimentalist fashion, Step allows users keep track of the propagation of user-inputted uncertainties over the course of a simulation. The mathematics behind this process is reviewed briefly here. As an example let’s look at the calculation for the uncertainty in kinetic energy in the Step’s ParticleErrors class,

* double ParticleErrors::kineticEnergyVariance() const {*

* return*

* _velocityVariance.dot(particle()->velocity().cwise().square()) * square(particle()->mass()) *

* + square(particle()->velocity().squaredNorm()/2) * *

* _massVariance;*

* }*

The kinetic energy (KE) of a particle is a function of both mass and velocity, where each of these variables have their respective uncertainties. In order to calculate the variance of kinetic energy we have to take the sum of d(KE)/dv multiplied by our velocityVariance and d(KE)/dm multiplied by our massVariance. The function only looks a bit confusing because we want a scalar value and we have to use a few Eigen functions to deal with 2D vectors.

The trouble with smoothed particle hydrodynamics is that each “fluid particle” is inherently an approximation of many particles or a bulk region of fluid. Nonetheless, the calculated densities and pressures for each chunk of fluid are subject to uncertainty. As outlined by Muller et al., any scalar quantity **A** can be calculated by summing over all particles **j** with some smoothing kernel defined by **W**:

Given the user-inputted uncertainty in particle mass **m** and whatever calculated uncertainty we have for our quantites **A** and **p**, this formula can be used with the method above to calculate uncertainty in the newly calculated quantity **A**. These calculations will be coded in the FluidParticleError class outlined below. Note the addition of the member variables for densityVariance and pressureVariance. These values could be calculated on the fly, but in doing there would be a large amount of calculations due to the summation of all nearby particle errors. Later in this project this will also include Viscosity error calculations that depend on the velocity of nearby particles using the same equation above.

It’s a week into my GSOC project and I still have lots of work to do. Next post will be a bit more software development oriented, as I’ll look more at the Qt** GUI** and how it connects with the numerical back-end of Step. Expect a post shortly!

*You can adjust the precision of your Solver in the properties dialog box of Step!