The prediction of measurement outcomes from an underlying structure often follows directly from fundamental physical principles. However, a fundamental challenge is posed when trying to solve the inverse problem of inferring the underlying source configuration based on measurement data. A key difficulty arises from the fact that such reconstructions often involve ill-posed transformations and that they are prone to numerical artifacts. Here, we develop a numerically efficient method to tackle this inverse problem for the reconstruction of magnetization maps from measured magnetic stray-field images. Our method is based on neural networks with physically inferred loss functions to efficiently eliminate common numerical artifacts. We report on a significant improvement in reconstruction over traditional methods and we show that our approach is robust to different magnetization directions, both in and out of plane, and to variations of the magnetic field measurement-axis orientation. While we showcase the performance of our method using magnetometry with nitrogen-vacancy center spins in diamond, our neural-network-based approach to solving inverse problems is agnostic to the measurement technique and thus is applicable beyond the specific use case demonstrated in this work.

This work evaluates the added benefit of using laser altimeter measurements for orbit reconstruction. As a spacecraft orbits a celestial body, its altimetry swaths progressively cross previous swaths. These locations, known as crossover points, yield valuable information about the orbited body and the spacecraft trajectory. The mathematical expressions for the inclusion of crossover measurements into orbit determination algorithms are presented and evaluated. It is shown that a first-order approximation of these expressions is insufficient and a more detailed expression is developed. To evaluate the impact of altimetry crossover measurements on orbit determination, the planetary mission Jupiter Icy moons Explorer (JUICE) by the European Space Agency (ESA) is used as a case study by means of a covariance analysis. In this initial analysis, the assumption is made that all altimetry measurements are obtained with nadir pointing which limits the direct applicability of our method until pointing is accounted for.