Jae Ho Chang

Dmitriy Y. Anistratov, Joseph M. Coale, James S. Warsa et al.

This paper presents multilevel iterative schemes for solving the multigroup Boltzmann transport equations (BTEs) with parallel calculation of group equations. They are formulated with multigroup and grey low-order equations of the Second-Moment (SM) method. The group high-order BTEs and low-order SM (LOSM) equations are solved in parallel. To further improve convergence and increase computational efficiency of algorithms Anderson acceleration is applied to inner iterations for solving the system of multigroup LOSM equations. Numerical results are presented to demonstrate performance of the multilevel iterative methods.

Jae Ho Chang, Massimiliano Russo, Subhadeep Paul

We consider the problem of transferring knowledge from a source, or proxy, domain to a new target domain for learning a high-dimensional regression model with possibly different features. Recently, the statistical properties of homogeneous transfer learning have been investigated. However, most homogeneous transfer and multi-task learning methods assume that the target and proxy domains have the same feature space, limiting their practical applicability. In applications, target and proxy feature spaces are frequently inherently different, for example, due to the inability to measure some variables in the target data-poor environments. Conversely, existing heterogeneous transfer learning methods do not provide statistical error guarantees, limiting their utility for scientific discovery. We propose a two-stage method that involves learning the relationship between the missing and observed features through a projection step in the proxy data and then solving a joint penalized regression optimization problem in the target data. We develop an upper bound on the method's parameter estimation risk and prediction risk, assuming that the proxy and the target domain parameters are sparsely different. Our results elucidate how estimation and prediction error depend on the complexity of the model, sample size, the extent of overlap, and correlation between matched and mismatched features.