Abstract: Disclosed herein are systems and methods for blind adaptive beamforming narrowband signals with an uncalibrated antenna array using machine learning. A machine-learning model in the spatial domain enhances the signal of interest and mitigates interfering signals. The systems and methods described herein do not require a calibrated antenna array RF chain. They are based on a neural network that learns the structure of the signal of interest and separates it from other signals in the time, frequency, and spatial domains, without the need of any additional information.

Abstract: Systems and methods for using compiler transforms to transform a non-local function into a local function are disclosed. The systems and methods perform a dynamic inter-procedural analysis before performing reverse-mode automatic differentiation. The dynamic inter-procedural analysis is performed to determine a maximum set of computer program information. A non-local to local transformation is applied to the determined maximum set of computer program information, and each original instruction is mapped to an optic that is represented as an opaque closure in the transformed local function.

Abstract: Methods, systems, and computing devices of a solver for detecting discontinuities or irregularities in a simulation in scientific computing is disclosed. The solver detects a discontinuity or irregularity using two phases: a crude phase, and a fine phase. The crude phase determines an estimate of where the discontinuity is located by extrapolating the interpolant. The fine phase iteratively refines the location of the discontinuity using root-finding and simplified Newton iterations. The solver disclosed herein takes fewer steps when running a simulation because it accurately steps over the discontinuities.