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HDNet: Physics-Inspired Neural Network for Flow Estimation based on Helmholtz Decomposition
June 14, 2024, 4:44 a.m. | Miao Qi, Ramzi Idoughi, Wolfgang Heidrich
cs.LG updates on arXiv.org arxiv.org
Abstract: Flow estimation problems are ubiquitous in scientific imaging. Often, the underlying flows are subject to physical constraints that can be exploited in the flow estimation; for example, incompressible (divergence-free) flows are expected for many fluid experiments, while irrotational (curl-free) flows arise in the analysis of optical distortions and wavefront sensing. In this work, we propose a Physics- Inspired Neural Network (PINN) named HDNet, which performs a Helmholtz decomposition of an arbitrary flow field, i.e., it …
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