Inverse design approaches that combine artificial intelligence and simulations are powerful tools for the characterization of fluid-mechanic phenomena in biology as well as in engineering. In this context, numerical solvers face many challenges. Algorithmically they must exhibit controllable accuracy, flexibility and adaptivity, while software-wise they must be able to achieve high performance on a multitude of hardware.
We develop numerics that allow for the seamless integration between artificial intelligence techniques such as evolutionary optimization or reinforcement learning and large scale simulations. Applications range from the characterization of bio-locomotion strategies to the group dynamics of energy harnessing devices.