Advanced diffusion models enable characterization of tissue microstructure with higher specificity than conventional DTI. While beyond DTI diffusion imaging have been found valuable in many studies, their clinical availability have been hampered mainly due to their long scan times. Furthermore, each diffusion model can only extract a few relevant microstructural features. Therefore, using multiple models helps to better understand the brain microstructure, which requires multiple expensive model-fitting. In this study, we use different deep learning approaches to jointly estimate multiple advanced diffusion maps from highly undersampled q-space data, which can reduce both the scan and processing times significantly.