Xin-jun Peng, Yi-fei Wang. Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression[J]. Chinese Journal of Chemical Physics , 2009, 22(5): 502-510. doi: 10.1088/1674-0068/22/05/502-510
Citation: Xin-jun Peng, Yi-fei Wang. Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression[J]. Chinese Journal of Chemical Physics , 2009, 22(5): 502-510. doi: 10.1088/1674-0068/22/05/502-510

Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression

doi: 10.1088/1674-0068/22/05/502-510
Funds:  This work was supported by the National Natural Science Foundation of China (No.30871341), the Na-tional High-Tech Research and Development Program of China (No.2006AA02-Z190), the Shanghai LeadingAcademic Discipline Project(No.S30405),and the Natural Science Foundation of Shanghai Normal University(No.SK200937).
  • Received Date: 2009-05-11
  • The stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous well-stirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity.In this work,a twin support vector regression based stochastic simulations algorithm (TS3A) is proposed by combining the twin support vector regression and SSA,the former is a well-known robust regression method in machine learning.Numeri-cal results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on effciency and accuracy with fewer simulating runs over the existing methods.
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Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression

doi: 10.1088/1674-0068/22/05/502-510
Funds:  This work was supported by the National Natural Science Foundation of China (No.30871341), the Na-tional High-Tech Research and Development Program of China (No.2006AA02-Z190), the Shanghai LeadingAcademic Discipline Project(No.S30405),and the Natural Science Foundation of Shanghai Normal University(No.SK200937).

Abstract: The stochastic simulation algorithm (SSA) accurately depicts spatially homogeneous well-stirred chemically reacting systems with small populations of chemical species and properly represents noise, but it is often abandoned when modeling larger systems because of its computational complexity.In this work,a twin support vector regression based stochastic simulations algorithm (TS3A) is proposed by combining the twin support vector regression and SSA,the former is a well-known robust regression method in machine learning.Numeri-cal results indicate that this proposed algorithm can be applied to a wide range of chemically reacting systems and obtain significant improvements on effciency and accuracy with fewer simulating runs over the existing methods.

Xin-jun Peng, Yi-fei Wang. Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression[J]. Chinese Journal of Chemical Physics , 2009, 22(5): 502-510. doi: 10.1088/1674-0068/22/05/502-510
Citation: Xin-jun Peng, Yi-fei Wang. Efficient Stochastic Simulation Algorithm for Chemically Reacting Systems Based on Support Vector Regression[J]. Chinese Journal of Chemical Physics , 2009, 22(5): 502-510. doi: 10.1088/1674-0068/22/05/502-510

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