Publications

Generating stable and metastable critical points in uncertain systems via flow‐based models

Wilson Callum, Vasile Massimiliano

Expert Systems Vol 43 (2026)

https://doi.org/10.1111/exsy.70196

Explainable decision support for conjunction risk analysis and the design of robust collision avoidance manoeuvres

Darm Paul, Riccardi Annalisa, Parsonage Ben, Wilson Callum, Vasile Massimiliano

76th International Astronautical Congress (2025)

Modelling future launch traffic and the associated risk to new missions

Wilson Callum, Vasile Massimiliano, Feng Jinglang, McNally Keiran, Maric Nina, Horstmann Andre

Advances in Space Research (2025)

https://doi.org/10.1016/j.asr.2025.07.055

Extending a risk metric for individual missions to evaluate overall risk in orbit

Wilson Callum, Vasile Massimiliano, Feng Jinglang, McNally Keiran, Maric Nina, Horstmann Andre

75th International Astronautical Congress (2024)

Generation and classification of critical points in uncertain N-body problems via machine learning

Wilson Callum, Vasile Massimiliano

75th International Astronautical Congress (2024)

Closing the loop between space capacity and life cycle assessment : a network-theoretic approach

Wang Yirui, Wilson Callum, Vasile Massimiliano, Wilson Andrew Ross

22nd IAA Symposium on Space Debris 22nd IAA Symposium on Space Debris, pp. 1012-1023 (2024)

https://doi.org/10.52202/078360-0093

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Professional Activities

75th International Astronautical Congress

Participant

14/10/2024

74th International Astronautical Congress

Participant

2/10/2023

73rd International Astronautical Congress 2022

Participant

18/9/2022

Enabling Intelligent Onboard Guidance, Navigation, and Control Using Near-Term Flight Hardware

Speaker

26/10/2021

72nd International Astronautical Congress

Participant

25/10/2021

Learning powered descent trajectories with optimal control demonstrations

Speaker

25/6/2021

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Projects

Generative Mapping and Control of Stationary Points in Complex Dynamical Systems

Vasile, Massimiliano (Principal Investigator) Wilson, Callum (Researcher)

Complex dynamical systems, from protein folding to brain dynamics, from complex networks to galaxies, present structures that are key to predicting and controlling their evolution. Among these structures, equilibrium points, periodic and libration solutions and resonances are of paramount importance to understand and control the evolution of these systems.

Identifying these structures is fundamental to ensure the resilience of national infrastructures, manufacture effective medication, understand and cure disease, study the evolution of climate on Earth, preserve our ability to access space. Just to name some key applications.

However, the identification of these structures in complex high dimensional system is a daunting task with a computational complexity that grows exponentially with the number of dimensions.

In this project we propose to use recent advances in generative machine learning to develop a new paradigm to automatically discover these structures and lay down the foundations of Generative Dynamics. The new paradigm, called MAX-net, has the potential to break the curse of dimensionality affecting many problems in scientific computing and provide a game changing enabling technology in all fields of engineering and science.

01-Jan-2023 - 30-Jan-2025

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