This paper presents a radial basis function based approach to generatesimplified models to estimate the trapping probability in optical trappingexperiments using offline simulations. The difference form of Langevin's equation is used to perform physically accurate simulations of a particleunder the influence of a trapping potential and is used to estimate trapping probabilities at discrete points in the parameter space. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models of trapping probability. We show that the proposed approach is computationally efficient in estimating the trapping probability and that the estimated probability using the simplified models is sufficiently close to the probability estimates from offline simulation data.
Friday, June 19, 2009
Generating Simplified Trapping Probability Models From Simulation of Optical Tweezers System
Banerjee AG, Balijepalli A, Gupta SK, LeBrun TW
This paper presents a radial basis function based approach to generatesimplified models to estimate the trapping probability in optical trappingexperiments using offline simulations. The difference form of Langevin's equation is used to perform physically accurate simulations of a particleunder the influence of a trapping potential and is used to estimate trapping probabilities at discrete points in the parameter space. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models of trapping probability. We show that the proposed approach is computationally efficient in estimating the trapping probability and that the estimated probability using the simplified models is sufficiently close to the probability estimates from offline simulation data.
This paper presents a radial basis function based approach to generatesimplified models to estimate the trapping probability in optical trappingexperiments using offline simulations. The difference form of Langevin's equation is used to perform physically accurate simulations of a particleunder the influence of a trapping potential and is used to estimate trapping probabilities at discrete points in the parameter space. Gaussian radial basis functions combined with kd-tree based partitioning of the parameter space are then used to generate simplified models of trapping probability. We show that the proposed approach is computationally efficient in estimating the trapping probability and that the estimated probability using the simplified models is sufficiently close to the probability estimates from offline simulation data.
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