ASCMO-STATIC

ETAS ASCMO-STATIC enables you to create data-based models that accurately represent the stationary behavior of complex systems. With a wealth of features and options for visualizing, analyzing, and optimizing system behavior, ASCMO-STATIC also supports the creation of experimental designs based on the DoE (Design of Experiments) methodology.

Using AI methods from the field of machine learning, ASCMO-STATIC allows you to accurately model complex relationships without requiring detailed knowledge of the underlying algorithms. Whether you're a less experienced user who appreciates parameter-free, automated model creation, or an expert who benefits from extensive configuration options, this software is designed to meet your needs.

A typical application of ASCMO-STATIC is the modeling of fuel consumption and pollutant emissions of complex internal combustion engines as a function of engine speed, load and all engine control variables. Based on these models, you can make accurate predictions and implement manual and automatic optimizations to achieve the best compromise between pollutant emissions, fuel consumption, and other operational constraints during engine operation.