The Instructed Glacier Model (IGM) simulates the ice dynamics, surface mass balance, and its coupling through mass conservation to predict the evolution of glaciers, icefields, or ice sheets.
The specificity of IGM is that it models the ice flow by a Convolutional Neural Network (CNN), which is trained with state-of-the-art ice flow models. By doing so, the most computationally demanding model component is substituted by a cheap emulator, permitting speed-up of several orders of magnitude at the cost of a minor loss in accuracy.
IGM consists of an open-source Python code, which runs across both CPU and GPU and deals with two-dimensional gridded input and output data. Together with a companion library of ice flow emulators, IGM permits user-friendly, highly efficient, and mechanically state-of-the-art glacier simulations.