Physics-informed neural networks for corrosion-fatigue prognosis

2 code implementations • Annual Conference of the PHM Society 2019 • Arinan Dourado, Felipe A. C. Viana

The result is a cumulative damage model where the physics-informed layers are used to model the relatively well understood physics (crack growth through Paris law) and the data-driven layers account for the hard to model effects (bias in damage accumulation due to corrosion).

Graph Regression Graph-to-Sequence +1