Another key aspect of PyRAT lies in the input partioning mechanisms that it employs. For lower dimensionality inputs, PyRAT is able to use input partioning as a mean to increase the precision of the analysis and prove a wider range of property. This partioning is reinforced by heuristics tailored to the abstract domains we use which allow a significant boost in precision on such networks<br>
Another key aspect of PyRAT lies in the input partitioning mechanisms that it employs. For lower dimensionality inputs, PyRAT is able to use input partitioning as a mean to increase the precision of the analysis and prove a wider range of property. This partitioning is reinforced by heuristics tailored to the abstract domains we use which allow a significant boost in precision on such networks<br>
As an example on the widely used and public benchmark ACAS-XU, PyRAT proves all the defined properties in 292s while state of the art tools such as nnenum takes 319s and neurify 1265s.
As an example on the widely used and public benchmark ACAS-XU, PyRAT proves all the defined properties in 292s while state of the art tools such as nnenum takes 319s and neurify 1265s.