Siddhant Agarwal

Siddhant Agarwal

Unravelling the Interior Evolution of Terrestrial Planets Through Machine Learning (2018 - )

Studying how rocky planets like Mercury, Venus, the Earth and Mars evolve over billions of years requires detailed modelling of mantle convection, the main driver of planetary evolution. The mantle - sandwiched between the crust and the core - behaves like a highly viscous fluid over geological time scales and hence can be quantified through equations describing conservation of mass, momentum and energy. These non-linear partial differential equations are typically solved numerically using fluid dynamics codes. However, the parameters and initial conditions to these equations are poorly known. Whereas certain outputs of the simulations (numerically solved equations) can be "observed'' via spacecraft missions and used to constrain key parameters and initial conditions, thus elucidating the basic physics and evolution of planets. Since each simulation can take from several hours to weeks to run, varying parameters extensively and repeatedly is often impractical. We aim to overcome this computational bottleneck by learning the mapping between parameters and observables through a combination of state-of-the-art geodynamic modelling, machine learning and high-performance computing.

Peer-reviewed Publications (journal or conference)

  1. S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, and G. Montavon (2020). A machine-learning-based surrogate model of Mars’ thermal evolution. Geophysical Journal International, 222(3), 1656-1670.  https://doi.org/10.1093/gji/ggaa234
  2. S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, and G. Montavon (2021). Towards constraining Mars’ thermal evolution using Machine Learning.  Earth and Space Science.https://doi.org/10.1029/2020EA001484

Other (presentations at conferences or preprints)

  1. S. Agarwal, N. Tosi, D. Breuer, S. Padovan, P. Kessel, and G. Montavon. Unravelling interior evolution of terrestrial planets using Machine Learning. (Oral presentation), Artificial Intelligence in Astronomy at ESO, Garching, Germany, 22-26 July 2019.
  2. S. Agarwal, N. Tosi, D. Breuer, P. Kessel, and G. Montavon. Using machine learning to predict 1D steady-state temperature profiles from compressible mantle convection simulations, (Oral presentation), 72nd Annual Meeting of the APS Division of Fluid Dynamics, Seattle, USA, 23-26 November 2019.
  3. S. Agarwal, N. Tosi, P. Kessel, D. Breuer, S. Padovan, and G. Montavon. Mars’ thermal evolution from machine-learning-based 1D surrogate modelling, (Oral presentation), EGU General Assembly, Online, 7 May 2020.
  4. S. Agarwal, N. Tosi, P. Kessel, D. Breuer, S. Padovan, and G. Montavon. Learning high dimensional surrogates from mantle convection simulations. (Oral presentation), 73rd Annual Meeting of the APS Division of Fluid Dynamics, Online, 23 November 2020.
  5. S. Agarwal, N. Tosi, P. Kessel, S. Padovan, D. Breuer, and G. Montavon. Towards constraining Mars' thermal evolution using machine learning, (PICO presentation), EGU General Assembly, Online, 19-30 Apr 2021, EGU21-4044, https://doi.org/10.5194/egusphere-egu21-4044