A classifier-guided sampling (CGS) method is introduced for solving engineering design optimization problems with discrete and/or continuous variables and continuous and/or discontinuous responses. The method m
[2] [Denoising Diffusion Implicit Models (DDIM) Sampling](https://nn.labml.ai/diffusion/stable_diffusion/sampler/ddim.html) [3] Yang Song, Jascha Sohl-Dickstein, Diederik P. Kingma, Abhishek Kumar, Stefano Ermon, and Ben Poole. Score-based generative modeling through stochastic differential equ...
1 INTRODUCTION In this paper, a classifier-guided sampling method is introduced for solving design optimization problems with discrete and/or continuous variables and potentially discontinuous responses. The method merges concepts from metamodel-guided sampling and population-based optimization algorithms to ...
Unlike conventional super-resolution (SR) approaches that typically generate LR images through synthetic downsampling, RISR confronts the complexity of real-world degradation. To effectively handle the intricate challenges of RISR, we adapt classifier-free guidance (CFG), a technique initially developed...