Proximity operations to small bodies, such as asteroids and comets, demand high levels of autonomy to achieve cost-effective, safe, and reliable Guidance, Navigation and Control (GNC) solutions. Enabling autonomous GNC capabilities in the vicinity of these targets is thus vital for future space applications. However, the highly non-linear and uncertain environment characterizing their vicinity poses unique challenges that need to be assessed to grant robustness against unknown shapes and gravity fields. In this paper, a pipeline designed to generate variable density gravity field models is proposed, allowing the generation of a coherent set of scenarios that can be used for design, validation, and testing of GNC algorithms. The proposed approach consists in processing a polyhedral shape model of the body with a given density distribution to compute the coefficients of the spherical harmonics expansion associated with the gravity field. To validate the approach, several comparison are conducted against analytical solutions, literature results, and higher fidelity models, across a diverse set of targets with varying morphological and physical properties. Simulation results demonstrate the effectiveness of the methodology, showing good performances in terms of modeling accuracy and computational efficiency. This research presents a faster and more robust framework for generating environmental models to be used in simulation and hardware-in-the-loop testing of onboard GNC algorithms.

This paper is aimed at using the newly developing field of physics informed machine learning (PIML) to develop models for predicting the remaining useful lifetime (RUL) aircraft engines. We consider the well-known benchmark NASA Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) data as the main data for this paper, which consists of sensor outputs in a variety of different operating modes. C-MAPSS is a well-studied dataset with much existing work in the literature that address RUL prediction with classical and deep learning methods. In the absence of published empirical physical laws governing the C-MAPSS data, our approach first uses stochastic methods to estimate the governing physics models from the noisy time series data. In our approach, we model the various sensor readings as being governed by stochastic differential equations, and we estimate the corresponding transition density mean and variance functions of the underlying processes. We then augment LSTM (long-short term memory) models with the learned mean and variance functions during training and inferencing. Our PIML based approach is different from previous methods, and we use the data to first learn the physics. Our results indicate that PIML discovery and solutions methods are well suited for this problem and outperform previous data-only deep learning methods for this data set and task. Moreover, the framework developed herein is flexible, and can be adapted to other situations (other sensor modalities or combined multi-physics environments), including cases where the underlying physics is only partially observed or known.

Neural networks that solve regression problems map input data to output data, whereas neural operators map functions to functions. This recent paradigm shift in perspective, starting with the original paper on the deep operator network or DeepONet [1, 2], provides a new modeling capability that is very useful in engineering - that is, the ability to replace very complex and computational resource-taxing multiphysics systems with neural operators that can provide functional outputs in real-time. Specifically, unlike other physics-informed neural networks (PINNs) [3] that require optimization during training and testing, a DeepONet does not require any optimization during inference, hence it can be used in realtime forecasting, including design, autonomy, control, etc. An architectural diagram of a DeepONet with the commonly used nomenclature for its components is shown in Figure 1. DeepONets can take a multi-fidelity or multi-modal input [4, 5, 6, 7, 8] in the branch network and can use an independent network as the trunk, a network that represents the output space, e.g. in space-time coordinates or in parametric space in a continuous fashion. In some sense, DeepONets can be used as surrogates in a similar fashion as reduced order models (ROMs) [9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19]. However, unlike ROMs, they are over-parametrized which leads to both generalizability and robustness to noise that is not possible with ROMs, see the recent work of [20].