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Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines.
A limited memory algorithm for bound constrained optimization
R. Byrd, P. Lu, J. Nocedal, and C. Zhu · 1995
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Philip K Maini, DL Sean McElwain, and David I Leavesley · 2004
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Journal of Computational Physics
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