• Corpus ID: 235703044

Physics-informed machine learning

@inproceedings{Wahlstrm2021PhysicsinformedML,
  title={Physics-informed machine learning},
  author={Niklas Wahlstr{\"o}m and Adrian G. Wills and Johannes N. Hendriks and Alexander Gregg and Christopher M. Wensrich and A. Solin and Simo S{\"a}rkk{\"a}},
  year={2021}
}
| Despite great progress in simulating multiphysics problems using the numerical discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate noisy data into existing algorithms, mesh generation remains complex, and highdimensional problems governed by parameterized PDEs cannot be tackled. Moreover, solving inverse problems with hidden physics is often prohibitively expensive and requires different formulations and elaborate computer codes. Machine learning… 

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