Discovering physics using signal processing

Compressive sensing (CS) is a novel technique developed recently in the field of signal processing. In signal processing, one samples a signal amplitude along the time axis and reconstructs it from the measured samples. In order to recover the measured signal one needs to satisfy the ``Shannon-Nyquist theorem'' which tells that the sampling rate should be at least twice the maximum frequency present in the signal. CS allows one to recover a sparse signal with a far fewer measurements than required by the Shannon-Nyquist theorem. We can utilize the CS paradigm to ``recover'' a physical model from just a few measurements or calculations[1]. In this talk, I will present a simple understanding of the concept of compressive sensing and its usage in realizing physical models. \\[4pt] [1] Lance J. Nelson, Gus L. W. Hart, Fei Zhou and Vidvuds Ozoli\c{n}\v{s}, ``Compressive sensing as a paradigm for building physical models,'' Phy. Rev. B. 87, 035125 (2013).