Mapping of dissipative particle dynamics in fluctuating hydrodynamics simulations

Dissipative particle dynamics (DPD) is a novel particle method for mesoscale modeling of complex fluids. DPD particles are often thought to represent packets of real atoms, and the physical scale probed in DPD models are determined by the mapping of DPD variables to the corresponding physical quantities. However, the non-uniqueness of such mapping has led to difficulties in setting up simulations to mimic real systems and in interpreting results. For modeling transport phenomena where thermal fluctuations are important (e.g., fluctuating hydrodynamics), an area particularly suited for DPD method, we propose that DPD fluid particles should be viewed as only 1) to provide a medium in which the momentum and energy are transferred according to the hydrodynamic laws and 2) to provide objects immersed in the DPD fluids the proper random "kicks" such that these objects exhibit correct fluctuation behaviors at the macroscopic scale. We show that, in such a case, the choice of system temperature and mapping of DPD scales to physical scales are uniquely determined by the level of coarse-graining and properties of DPD fluids. We also verified that DPD simulation can reproduce the macroscopic effects of thermal fluctuation in particulate suspension by showing that the Brownian diffusion of solid particles can be computed in DPD simulations with good accuracy

Modulating Image Restoration with Continual Levels via Adaptive Feature Modification Layers

In image restoration tasks, like denoising and super resolution, continual modulation of restoration levels is of great importance for real-world applications, but has failed most of existing deep learning based image restoration methods. Learning from discrete and fixed restoration levels, deep models cannot be easily generalized to data of continuous and unseen levels. This topic is rarely touched in literature, due to the difficulty of modulating well-trained models with certain hyper-parameters. We make a step forward by proposing a unified CNN framework that consists of few additional parameters than a single-level model yet could handle arbitrary restoration levels between a start and an end level. The additional module, namely AdaFM layer, performs channel-wise feature modification, and can adapt a model to another restoration level with high accuracy. By simply tweaking an interpolation coefficient, the intermediate model - AdaFM-Net could generate smooth and continuous restoration effects without artifacts. Extensive experiments on three image restoration tasks demonstrate the effectiveness of both model training and modulation testing. Besides, we carefully investigate the properties of AdaFM layers, providing a detailed guidance on the usage of the proposed method.Comment: Accepted by CVPR 2019 (oral); code is available: https://github.com/hejingwenhejingwen/AdaF