Efficient Numerical Wave Propagation Enhanced by an End-to-End Deep Learning Model

In a variety of scientific and engineering domains, ranging from seismic modeling to medical imaging, the need for high-fidelity and efficient solutions for high-frequency wave propagation holds great significance. Recent advances in wave modeling use sufficiently accurate fine solver outputs to train neural networks that enhance the accuracy of a fast but inaccurate coarse solver. A stable and fast solver further allows the use of Parareal, a parallel-in-time algorithm to retrieve and correct high-frequency wave components. In this paper we …

accuracy advances cs.lg deep learning domains engineering fidelity imaging math.ap medical medical imaging modeling networks neural networks numerical propagation significance solutions solver train