14 Papers
7 Citations
Jun Jia is an academic researcher from Oak Ridge National Laboratory. The author has contributed to research in topics: Multiresolution analysis & Solver. The author has an hindex of 10, co-authored 14 publications. Previous affiliations of Jun Jia include University of North Carolina at Chapel Hill.
Chat about Author
Papers
Accelerating the convergence of spectral deferred correction methods
TL;DR: It is shown that for linear problems, the iterations in the SDC algorithm are equivalent to constructing a preconditioned Neumann series expansion for the solution of the standard collocation discretization of the ODE.
160
MADNESS: A Multiresolution, Adaptive Numerical Environment for Scientific Simulation
Robert W. Harrison,Gregory Beylkin,Florian A. Bischoff,Justus A. Calvin,George I. Fann,Jacob Fosso-Tande,Diego Galindo,Jeff R. Hammond,Rebecca Hartman-Baker,Judith Hill,Jun Jia,Jakob S. Kottmann,M-J. Yvonne Ou,Laura E. Ratcliff,Matthew G. Reuter,Adam Richie-Halford,Nichols A. Romero,Hideo Sekino,William A. Shelton,Bryan Sundahl,W. Scott Thornton,Edward F. Valeev,Álvaro Vázquez-Mayagoitia,Nicholas Vence,Yukina Yokoi +24 more
TL;DR: The features and capabilities of MADNESS are described and some current applications in chemistry and several areas of physics are discussed.
93
Arbitrary order Krylov deferred correction methods for differential algebraic equations
TL;DR: A new framework for the construction of accurate and efficient numerical methods for differential algebraic equation (DAE) initial value problems is presented, based on applying spectral deferred correction techniques as preconditioners to a Picard integral collocation formulation for the solution.
92
Krylov deferred correction accelerated method of lines transpose for parabolic problems
Jun Jia,Jingfang Huang +1 more
TL;DR: Preliminary numerical experiments show that the KDC accelerated MoL T technique is unconditionally stable, can be spectrally accurate in both temporal and spatial directions, and allows optimal time-stepsizes in long-time simulations.
49
MADNESS applied to density functional theory in chemistry and nuclear physics
George I. Fann,Robert W. Harrison,Gregory Beylkin,Jun Jia,R Hartman-Baker,William A. Shelton,S Sugiki +6 more
- 01 Jul 2007
TL;DR: In this article, a multiresolution numerical method for solving quantum chemistry and nuclear physics problems based on Density Functional Theory (DFT) was developed using low separation rank representations of functions and operators in conjunction with representations in multiwavelet bases.
19