Signaloid Cloud Compute Engine

Use Case Family:

Engineering

### Use Case

# Materials Engineering

Uncertainties in empirical model parameters affect the uncertainty distribution of the model's output, for a model of a physical process. For example, for the materials precipitate "cutting" dislocation model from Brown and Ham, metallurgists can gain insight into the uncertainty of a model of a metal alloy's strength. The use case highlights how the Signaloid C0 processor allows you to take unmodified programs and track uncertainty through them, getting all the benefits that you would usually only be able to obtain from a hand-crafted (and time-consuming) Monte Carlo evaluation.

### Note 1:

The underlying distribution representations are not literal histograms: The distribution plots use an adaptive algorithm to render a mutually-consistent and human-interpretable depiction for both the Signaloid distribution representations and the Monte Carlo samples, to permit qualitative comparison.

### Note 2:

Because Monte Carlo works by statistical sampling, each set of multi-iteration Monte Carlo runs (e.g., each time a 200k-iteration Monte Carlo is run) will result in a slightly different final distribution. By contrast, the results from Signaloid's platform are completely deterministic and yield the same distribution each time, for a given Signaloid C0 core type. The performance improvement over Monte Carlo results above show the performance speedup of running on Signaloid's platform, compared to running a Monte Carlo on an AWS r7iz high-performance AWS instance, for the same quality of distribution while accounting for the variations inherent in Monte Carlo. To compare the quality of distribution, we run a large Monte Carlo until convergence (e.g., 1M iterations) and use this as a baseline or ground truth reference for distribution quality (not for performance). We then compare performance of the Signaloid solution against a Monte Carlo iteration count for which the output distributions of 100 out of 100 repetitions are all at smaller Wasserstein distance (than the Signaloid-core-executed algorithm's output distribution) to the output distribution of the baseline reference. Intuitively, this analysis gives the Monte Carlo iteration count that results in an output distribution that is never worse than the Signaloid-core-executed computation's output distribution. 2.9x speedup achieved with TTR-32-OFF. Performance data based on Spring 2024 release of Signaloid's technology.