Understanding the physical processes that control the evolution and present-day state of terrestrial planets around the Sun (Mercury, Venus, Earth, Moon, and Mars) and in extrasolar systems requires computationally-intensive forward models of the thermal history of the interior. These models are constrained by spacecraft- and telescope-based observations bearing information on interior processes, such as gravity and magnetic fields, surface topography, and composition. Yet the problem of inferring the interior evolution from surface observables is severely underdetermined: a large number of parameters – initial conditions as well as material properties – are poorly known and need to be systematically varied. The application of advanced machine learning (ML) (e.g. deep learning algorithms) can obviate the need to perform extensive explorations of the parameter space, which are often impractical if not impossible. We combine state-of-the-art forward models of the thermal evolution of terrestrial planets with machine learning algorithms with the goal of identifying and constraining the key parameters controlling the planetary evolution. Through this joint approach we will develop an innovative computational framework for the interpretation of the growing amount of data delivered by spacecraft missions to planetary bodies of the solar system and by telescope missions aimed at detecting planets orbiting other stars.
- Siddhant Agarwal, Nicola Tosi, Doris Breuer, Sebastiano Padovan, Pan Kessel,Grégoire Montavon. Unravelling interior evolution of terrestrial planets using Machine Learning, Oral presentation at Artificial Intelligence in Astronomy at ESO, Garching, Germany, 22-26 July 2019.
- Siddhant Agarwal, Nicola Tosi, Doris Breuer, Pan Kessel, Grégoire Montavon. Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations, Oral presentation at 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, USA, 23-26 November 2019.