Integration of AI methods with simulation workflows to accelerate, approximate, analyze, or improve computational experiments.
Possible approaches include surrogate models, learned approximations, parameter estimation, simulation analysis, and hybrid computational systems.
Applications include engineering simulation, environmental modeling, biological systems, physical processes, and computational experiments.
Implementation is selected according to the available data, technical constraints, required level of automation, and the existing software or research environment. The solution can be implemented as a standalone component or integrated into a larger system.
The resulting system is intended to provide a clear computational workflow that can be evaluated, maintained, and extended as the project develops. Model choice, data processing, interfaces, and deployment can therefore be adapted to the requirements of the specific project.