Memory reduction for numerical solution of differential equations using compressive sensing

@article{Unni2017MemoryRF,
  title={Memory reduction for numerical solution of differential equations using compressive sensing},
  author={Midhun P Unni and M. Girish Chandra and A. Anil Kumar},
  journal={2017 IEEE 13th International Colloquium on Signal Processing \& its Applications (CSPA)},
  year={2017},
  pages={79-84}
}
Mathematical description of our physical world revolves in a great deal around partial and ordinary differential equations (PDES/ODEs). May it be the case of modelling cardiovascular system or quantum electrodynamics, solving a system of PDEs/ODEs, including their coupled forms is indispensable. It is known that many of these system of DEs does not have a closed form solution and need to be solved by a computer. It takes a large amount of memory in saving the state variables as they evolve in a… 

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