Physics-informed neural networks with hard constraints for inverse design

@article{Lu2021PhysicsinformedNN,
  title={Physics-informed neural networks with hard constraints for inverse design},
  author={Lu Lu and Rapha{\"e}l Pestourie and Wenjie Yao and Zhicheng Wang and Francesc Verdugo and Steven G. Johnson},
  journal={SIAM J. Sci. Comput.},
  year={2021},
  volume={43},
  pages={B1105-B1132}
}
Inverse design arises in a variety of areas in engineering such as acoustic, mechanics, thermal/electronic transport, electromagnetism, and optics. Topology optimization is a major form of inverse design, where we optimize a designed geometry to achieve targeted properties and the geometry is parameterized by a density function. This optimization is challenging, because it has a very high dimensionality and is usually constrained by partial differential equations (PDEs) and additional… 
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