Since the first discoveries of extrasolar planets in the 1990s, more than 4000 exoplanets have been discovered to date, and the number is growing rapidly with new dedicated space and ground-based surveys. From radius measurements via transit observations and mass estimations via radial velocity measurements, the inner structure of planets can be modeled numerically. This characterization is crucial for our understanding of the diversity of the observed planets, their formation processes, and the question whether or not they can support life. However, even with accurate radius and mass measurements, many different solutions for the internal structure can be found, since the relative proportions of iron, silicates, water ice, and volatile elements are not known.

The goal of this project is to implement machine-learning-based approaches to infer planetary interiors based on observational data, and use those to identify potentially observable parameters that can better constrain the range of possible interior structures. Machine learning can avoid the need for extensive interior modeling for each individual exoplanet by learning from large sets of precalculated data generated with suitable forward models. We aim to develop such an inference framework for the fast characterization of planetary interiors. For a comprehensive view of a planet's evolution, we will link thermal evolution models of the interior to models of atmospheric evolution, and aim at including results from population synthesis modeling. These models contain essential information on the structure, composition and evolution history of planets, linking the planet interior to the star system they reside in, and supply us with a large data set of synthetic planets that have formed under the physical constraints of their formation model.

The result of this project will be a comprehensive inference model capable of rapidly determining the range of physically meaningful interiors of observed exoplanets, which will open up new possibilities for finding observable parameters that are particularly important in constraining possible internal structures.