5 papers
From Atoms to Entropy: Optimal Noise Allocation for Diffusion Training in the Convex Regime
Luca Ambrogioni, Giulio Franzese, Alberto Foresti +7
How should a diffusion model decide which noise levels to train on, and how much? Despite the importance of this choice, current noise schedules are based largely on heuristics or…
Noise Scheduling as Information-Guided Allocation in Diffusion Training
Gabriel Raya, Bac Nguyen, Georgios Batzolis +6
We introduce InfoNoise, an online adaptive noise schedule for diffusion training that reallocates optimization effort toward noise levels where denoising is most informative. Toget…
Stabilizing Physics-Informed Consistency Models via Structure-Preserving Training
Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2
We propose a physics-informed consistency modeling framework for solving partial differential equations (PDEs) via fast, few-step generative inference. We identify a key stability…
TINNs: Time-Induced Neural Networks for Solving Time-Dependent PDEs
Chen-Yang Dai, Che-Chia Chang, Te-Sheng Lin +2
Physics-informed neural networks (PINNs) solve time-dependent partial differential equations (PDEs) by learning a mesh-free, differentiable solution that can be evaluated anywhere…
Consistency Training with Physical Constraints
Che-Chia Chang, Chen-Yang Dai, Te-Sheng Lin +2
We propose a physics-aware Consistency Training (CT) method that accelerates sampling in Diffusion Models with physical constraints. Our approach leverages a two-stage strategy: (1…