Everything perfectly coordinated WITH

STOCHOS

Stochos comprises a wide variety of algorithms in the areas of optimization, sensitivity analysis, design of experiment, robustness and reliability analysis and robust design optimization. All procedures were perfectly matched to the use of DIM-GP as the underlying model. As a result, our methods achieve maximum performance and efficiency.

One of our goals is to make the use of complex algorithms as easy as possible. That means everything can be applied to a large number of problems without major adjustments. No expert knowledge is necessary either. All of our products are written in Python and are therefore very easy to integrate into existing software platforms and workflows.

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Bayesian optimization

With this type of optimization, the model uncertainty is also taken into account in order to plan the next experiments where the model is either particularly uncertain or where there is a high probability that an improvement will be assumed. This means that even very complex optimization problems can be solved very efficiently.

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Sensitivity analysis

With our methods all kinds of dependencies can be determined. As a result, a deeper understanding of the relationships between input and output parameters can be recognized very easily and this also in very high-dimensional parameter spaces.

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Adaptive Design of Experiment

Compared to classic test planning, our adaptive test plans only suggest experiments in those areas that lead to an improvement in the optimization goals (also Pareto-optimal), compliance with the constraints, more precise calculation of robustness and reliability and an increase in the model prognosis. As a result, only as much data is generated as the machine learning model, optimization and stochastic analysis actually need. Another advantage is that the model learns which areas are permitted in the design space and which do not lead to any result.

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stochos RDO

Robust design optimization

In a robust design optimization (RDO), you can incorporate possible uncertainties in your input and process parameters. This not only improves your target figures, but also makes them more robust against possible, unforeseen influences. The same of course also applies to possible secondary conditions that have to be observed, which makes a failure of your products more avoidable.

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Reliability analysis

With the help of a reliability analysis, you can determine the likelihood of certain events occurring and thus how reliable your product is. We can also analyze which of your parameters has the greatest impact on the reliability of your product. This gives you the information you need to better assess certain risks.

DSTheoretical