mlsanityData to 14 Oct 2026

Selected Trends in Artificial Intelligence for Space Applications

arXiv 2212.06662 ↗First posted 10 Dec 2022cs.LG astro-ph.IM cs.AIcited by 5

Not on the map. Why?

Abstract (arXiv)

The development and adoption of artificial intelligence (AI) technologies in space applications is growing quickly as the consensus increases on the potential benefits introduced. As more and more aerospace engineers are becoming aware of new trends in AI, traditional approaches are revisited to consider the applications of emerging AI technologies. Already at the time of writing, the scope of AI-related activities across academia, the aerospace industry and space agencies is so wide that an in-depth review would not fit in these pages. In this chapter we focus instead on two main emerging trends we believe capture the most relevant and exciting activities in the field: differentiable intelligence and on-board machine learning. Differentiable intelligence, in a nutshell, refers to works making extensive use of automatic differentiation frameworks to learn the parameters of machine learning or related models. Onboard machine learning considers the problem of moving inference, as well as learning, onboard. Within these fields, we discuss a few selected projects originating from the European Space Agency's (ESA) Advanced Concepts Team (ACT), giving priority to advanced topics going beyond the transposition of established AI techniques and practices to the space domain.

Key facts. Cited by 5 papers in the map (data through 14 Oct 2026). Builds on 26 of its 74 references that are in the map. First posted on arXiv 10 Dec 2022.

Builds on

26 of 74 references.

References · 26

  1. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  2. 2016Communication-Efficient Learning of Deep Networks from Decentralized Datacited by 7,669
  3. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  4. 2021EfficientNetV2: Smaller Models and Faster Trainingcited by 1,135
  5. 2014Neural Turing Machinescited by 644
  6. 2019Surrogate Gradient Learning in Spiking Neural Networkscited by 626
  7. 2021Carbon Emissions and Large Neural Network Trainingcited by 567
  8. 2021Physics-informed neural networks (PINNs) for fluid mechanics: A reviewcited by 473
  9. 2019Federated Learning in Mobile Edge Networks: A Comprehensive Surveycited by 408
  10. 2016The CMA Evolution Strategy: A Tutorialcited by 377
  11. 2020B-PINNs: Bayesian Physics-Informed Neural Networks for Forward and Inverse PDE Problems with Noisy Datacited by 335
  12. 2021Shape As Points: A Differentiable Poisson Solvercited by 187
14 more
  1. 2021Physics-based Deep Learningcited by 68
  2. 2020Expressivity of Deep Neural Networkscited by 32
  3. 2016Real-time optimal control via Deep Neural Networks: study on landing problemscited by 28
  4. 2021On-Board Federated Learning for Dense LEO Constellationscited by 22
  5. 2022Federated Learning in Satellite Constellationscited by 21
  6. 2018Automatic differentiation in ML: Where we are and where we should be goingcited by 20
  7. 2021MSMatch: Semi-Supervised Multispectral Scene Classification with Few Labelscited by 12
  8. 2018On the stability analysis of deep neural network representations of an optimal state-feedbackcited by 10
  9. 2019Aggressive Online Control of a Quadrotor via Deep Network Representations of Optimality Principlescited by 10
  10. 2021On Board Volcanic Eruption Detection through CNNs and Satellite Multispectral Imagerycited by 10
  11. 2022Neural representation of a time optimal, constant acceleration rendezvouscited by 10
  12. 2019Learning the optimal state-feedback via supervised imitation learningcited by 8
  13. 2022Reliable event detection for Taylor methods in astrodynamicscited by 5
  14. 2021Geodesy of irregular small bodies via neural density fields: geodesyNetscited by 4

Built on by

Cited by year

Citing papers per year: 2023: 1; 2024: 3; 2025: 1.

5 papers.

  1. 2024Satellite Federated Edge Learning: Architecture Design and Convergence Analysiscited by 10
  2. 2024Machine Learning in Space: Surveying the Robustness of on-board ML models to Radiationcited by 3
  3. 2024Energy-Aware Federated Learning in Satellite Constellationscited by 3
  4. 2023Investigation of the Robustness of Neural Density Fieldscited by 2
  5. 2025Memristor-Based Neural Network Accelerators for Space Applications: Enhancing Performance with Temporal Averaging and SIRENscited by 1