We’re very happy to report that the Thuerey research group has very recently been award a so-called “Proof of Concept” grant by the European Research Council (ERC).
We will leverage deep convolutional neural networks (CNNs) with physically-based architectures and loss functions for a first deep learning based flow solver. From the ERC Starting Grant realFlow a first algorithmic realization exists, which provides the core technology that will be taken to the next level within this PoC. Specifically, we plan to employ this technology for a prediction of Reynolds-averaged turbulence flows in order to achieve interactive runtimes for complex simulations that previously took long computing times. However, instead of aiming for general purpose solvers, we will target specific application areas with targeted trained models. This technology has the potential to fundamentally change the way designers and engineers can work with physics simulations to get feedback for their designs. It will also make these simulations available to smaller companies in the value chain that previously were not able to fund and maintain complex simulators.
In parallel, our goal is to establish an open platform for exchanging data and trained models for physics simulations. We believe that open standards will on the one hand support the adoption of the new technology, while at the same time providing publicity and marketing opportunities for products to be developed alongside this platform. In particular, the deep learning based turbulence solver will make use of the open data and model formats. In the long run, this will make it possible to incorporate the trained model into new applications, e.g., for solving inverse problems in the context of flow simulations.