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  author =        {Karniadakis, George Em and Kevrekidis, Ioannis G. and
                   Lu, Lu and Perdikaris, Paris and Wang, Sifan and
                   Yang, Liu},
  journal =       {Nature Reviews Physics},
  month =         {Jun},
  number =        {6},
  pages =         {422-440},
  title =         {Physics-informed machine learning},
  volume =        {3},
  year =          {2021},
  abstract =      {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 high-dimensional 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 has emerged as a promising alternative, but
                   training deep neural networks requires big data, not
                   always available for scientific problems. Instead,
                   such networks can be trained from additional
                   information obtained by enforcing the physical laws
                   (for example, at random points in the continuous
                   space-time domain). Such physics-informed learning
                   integrates (noisy) data and mathematical models, and
                   implements them through neural networks or other
                   kernel-based regression networks. Moreover, it may be
                   possible to design specialized network architectures
                   that automatically satisfy some of the physical
                   invariants for better accuracy, faster training and
                   improved generalization. Here, we review some of the
                   prevailing trends in embedding physics into machine
                   learning, present some of the current capabilities
                   and limitations and discuss diverse applications of
                   physics-informed learning both for forward and
                   inverse problems, including discovering hidden
                   physics and tackling high-dimensional problems.},
  doiurl =        {10.1038/s42254-021-00314-5},
  issn =          {2522-5820},
  url =           {https://doi.org/10.1038/s42254-021-00314-5},
}

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  title =         {Extreme states of matter on Earth and in space},
  volume =        {52},
  year =          {2009},
  url =           {https://dx.doi.org/10.3367/UFNe.0179.200906h.0653},
}

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@article{egorova2019,
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@misc{SciPy,
  note =          {SciPy \href{https://scipy.org} {https://scipy.org}},
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@article{cai2017spall,
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  number =        {10},
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}


