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Our resesarch in magnetism, electrochemistry, and computational
biology poses many problems that are characterized by a wide
separation of timescales between microscopic scales (femtosecond to
nanosecond) and macroscopic ones (milliseconds to millions of
years). Developing simulation algorithms that can bridge these huge
gaps between fast and slow timescales is one of the biggest
challenges in computational science. As a result, successfull
algorithms have potential applications for a wide range of
applications in physical, chemical, biological, and social
sciences. Among the algorithms developed in our group are the Monte
Carlo with Absorbing Markov Chains (MCAMC) [M.A. Novotny, Physical
Review Letters 75, 1424 (1995)] and the Projective Dynamics (PD)
method [M. Kolesik, M.A. Novotny, and P.A. Rikvold, Physical Review
Letters 80, 3384 (1998); International Journal of Modern Physics C
14, 121 (2003)]. These faster-than-real-time algorithms can speed
up Monte Carlo simulations of processes with widely disparate
timescales, such as magnetization switching, by many orders of
magnitude without changing the details of the underlying,
physical dynamics.
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