A Neural Differentiable Solver for Efficient Simulation of Fluid-Structure Interaction

Solving complex fluid-solid interactions (FSI) phenomena is crucial in many science and engineering applications. Classical CFD-based solvers are too expensive to tackle the large-scale simulation demands. The ever-increasing data availability and rapid developments in deep learning (DL) have opened new avenues to tackle the challenges by integrating deep neural networks (DNN) into traditional numerical solvers, enabling effective data-driven modeling. In this regard, we established a fully differentiable programming framework for simulating FSI problems based on JAX. The fluid is solved by direct numerical simulation (DNS), and the solid is immersed in the fluid field through the direct forcing method. Specifically, the velocity inside an immersed solid is interpolated by a sinusoidal function. As the framework is entirely built in JAX with auto-differentiation (AD) capability, different DNN models can be easily integrated and optimized within the numerical solver as a whole in an end-to-end manner. Several benchmark cases are studied to demonstrate the merit and potential of the proposed method for efficient FSI modeling.