Orateur
Description
Unmixing problems are ubiquitous in Physics, ranging from spectral unmixing to unsupervised component separation, where elementary physical components need to be separated out from intricate observed mixtures. These problems are generally ill-posed, which mandates the design of effective regularisation to better distinguish between the sought-after components. While ML-based methods are promising, their application is very often limited to the scarcity of the training samples (e.g. very few observations, very high cost of physical simulations, etc.). We first propose using a special type of autoencoder (AE), coined interpolatory AE, to learn adapted representations for the components to be retrieved, from very few training samples. We show how such representations can be plugged into traditional solvers to tackle unmixing problems. This will be illustrated with applications in X-ray astrophysics and spectrometry in nuclear Physics.
