SCIENTIFIC COMPUTING PUBLICATIONS
Vassiliou A., Sandberg, K., Belkin, G., Lewis, R.D., Popovic, I., and Woog, L., SEG Expanded Abstracts 2015 Technical Program, Society of Exploration Geophysicists, (2014)
Synopsis: The paper describes a fast computational method of velocity estimation in the temporal frequency domain of seismic data.
Well-posedness and Numerical Solution of a Nonlinear Volterra Partial Integro-differential Equation Modeling a Swelling Porous Material
Wojciechowski, K.J., Chen, J., Schreyer-Bennethum, and Sandberg, K., Journal of Porous Media, 17(9), p. 763-784, (2014)
Synopsis: The paper describes a pseudo-spectral based numerical method for solving a non-linear initial-boundary problem in polar geometry. The method is applied to a Volterra integrodifferential equation used to model the behavior of swolleng porous material.
Beylkin, G. and Sandberg, K., Journal of Computational Physics, 265, p. 156-171, (2014)
Synopsis: This paper describes a symplectic and A-stable and highly accurate numerical scheme for solving ODEs, with emphasis on equations from celestial mechanics.
Bradley, B.K., Jones, B.A., Beylkin, G., Sandberg, K., and Axelrad, P. Celestial Mechanics and Dynamical Astronomy, 119(2), p. 143-168, (2014)
Synopsis: This paper applies the numerical method from the paper “ODE-solvers Using Band-limited Approximations” to construct an efficient and highly accurate solution to the problem of orbit propagation.
Sandberg, K. and Wojciechowski, K.J., Journal of Computational Physics, 230(15), p. 5836–5863, (2011)
Synopsis: This paper describes a method for constructing pseudo-spectral derivatives with boundary conditions incorporated. The advantage of the described approach is that it provides a matrix norm that scales optimally with size, which enables significantly larger time steps in PDE schemes than traditional pseudo-spectral derivative operators.
Sandberg, K., Lecture Notes in Computer Science (LNCS), 6454, p. 107-116 (2010)
Synopsis: This paper describes a simple, yet extremely powerful method for denoising, enhancing, and segmenting curve-like structures in images. The method is based on the notion of curve fields and the Curve Field Transform (CFT) (a.k.a. the Curve Filter Transform). For movie illustrating the Curve Field Transform, click here.
in Object Modeling, Algorithms, and Applications, Barneva, R.P. et al Ed., Research Publishing (2010)
Synopsis: This paper generalizes earlier work of using orientation fields for segmenting line-like structures, to more general curve-like structures.
Sandberg, K., Lecture Notes in Computer Science (LNCS), 5875, p. 564-575 (2009)
Synopsis: This paper describes a method for generating orientation fields of images, and using such fields to enhance, denoise, and segment lines and curves in images.