2 papers
cs.RO2024
Denoising Diffusion Planner: Learning Complex Paths from Low-Quality Demonstrations
Michiel Nikken, Nicolò Botteghi, Wesley Roozing +1
Denoising Diffusion Probabilistic Models (DDPMs) are powerful generative deep learning models that have been very successful at image generation, and, very recently, in path planni…
cs.LG2024
Recurrent Deep Kernel Learning of Dynamical Systems
Nicolò Botteghi, Paolo Motta, Andrea Manzoni +2
Digital twins require computationally-efficient reduced-order models (ROMs) that can accurately describe complex dynamics of physical assets. However, constructing ROMs from noisy…