Plenary – “Hybrid modeling: best of both worlds?” (P. Gentine)
In recent years, we have witnessed an explosion in the applications of machine learning, especially for environmental problems. Yet for broader use, those algorithms may need to respect exactly some physical constraints such as the conservation of mass and energy.
In addition, environmental applications (e.g. drought, heat waves) are typically focusing on extremes and on out-of-sample generalization rather than on interpolation. This can be a problem for typical algorithms, which interpolate well but have difficulties extrapolating.
I will here show how a hybridization of machine learning algorithms, imposing physical knowledge within them, can help with those different issues and offer a promising avenue for climate applications and process understanding.