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Found 4 papers, 2 papers with code
Accelerating the discovery of steady-states of planetary interior dynamics with machine learning
Consequently, achieving steady-state requires a large number of time steps due to the disparate time scales governing the stagnant and convecting regions.
ExoMDN: Rapid characterization of exoplanet interior structures with Mixture Density Networks
The model is trained on a large dataset of more than 5. 6 million synthetic planets below 25 Earth masses consisting of an iron core, a silicate mantle, a water and high-pressure ice layer, and a H/He atmosphere.
Deep learning for surrogate modelling of 2D mantle convection
Using a dataset of 10, 525 two-dimensional simulations of the thermal evolution of the mantle of a Mars-like planet, we show that deep learning techniques can produce reliable parameterized surrogates (i. e. surrogates that predict state variables such as temperature based only on parameters) of the underlying partial differential equations.
Settling of inertial particles in turbulent Rayleigh-Benard convection
The settling behaviour of small inertial particles in turbulent convection is a fundamental problem across several disciplines, from geophysics to metallurgy.
Fluid Dynamics Soft Condensed Matter Geophysics
