An iterative data-driven turbulence modeling framework based on Reynolds stress representation

Data-driven turbulence modeling studies have reached such a stage that the fundamental framework is basically settled, but several essential issues remain that strongly affect the performance, including accuracy, smoothness, and generalization capacity. Two problems are studied in the current research: (1) the processing of the Reynolds stress tensor and (2) the coupling method between the machine learning turbulence model and CFD solver. The first determines the form of predicting targets and the resulting physical completeness and interpretability. The second determines the training process and intrinsic relevance between the mean flow features and Reynolds stress. For the Reynolds stress processing issue, we perform the theoretical derivation to extend the relevant tensor arguments of Reynolds stress in addition to the strain rate and rotation rate. Then, the tensor representation theorem is employed to give the complete irreducible invariants and integrity basis. In addition, an adaptive regularization term is employed to enhance the representation performance. For the CFD coupling issue, an iterative coupling data-driven turbulence modeling framework with consistent convergence is proposed. The training data preparation, predicting target selection, and computation platform are illustrated. The framework is then applied to a canonical separated flow for verification. The mean flow results obtained by coupling computation of the trained machine learning model and CFD solver have high consistency with the DNS true values, which proves the validity of the current approach

Understanding Deep Architectures with Reasoning Layer

Recently, there has been a surge of interest in combining deep learning models with reasoning in order to handle more sophisticated learning tasks. In many cases, a reasoning task can be solved by an iterative algorithm. This algorithm is often unrolled, and used as a specialized layer in the deep architecture, which can be trained end-to-end with other neural components. Although such hybrid deep architectures have led to many empirical successes, the theoretical foundation of such architectures, especially the interplay between algorithm layers and other neural layers, remains largely unexplored. In this paper, we take an initial step towards an understanding of such hybrid deep architectures by showing that properties of the algorithm layers, such as convergence, stability, and sensitivity, are intimately related to the approximation and generalization abilities of the end-to-end model. Furthermore, our analysis matches closely our experimental observations under various conditions, suggesting that our theory can provide useful guidelines for designing deep architectures with reasoning layers.Comment: 34th Conference on Neural Information Processing Systems (NeurIPS 2020

Highly Selective and Stable Isolated Non-Noble Metal Atom Catalysts for Selective Hydrogenation of Acetylene

ACKNOWLEDGMENTS This work was financially supported by National Natural Science Foundation of China (21908002) and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2108).Peer reviewedPostprin

Affordance Diffusion: Synthesizing Hand-Object Interactions

Recent successes in image synthesis are powered by large-scale diffusion models. However, most methods are currently limited to either text- or image-conditioned generation for synthesizing an entire image, texture transfer or inserting objects into a user-specified region. In contrast, in this work we focus on synthesizing complex interactions (ie, an articulated hand) with a given object. Given an RGB image of an object, we aim to hallucinate plausible images of a human hand interacting with it. We propose a two-step generative approach: a LayoutNet that samples an articulation-agnostic hand-object-interaction layout, and a ContentNet that synthesizes images of a hand grasping the object given the predicted layout. Both are built on top of a large-scale pretrained diffusion model to make use of its latent representation. Compared to baselines, the proposed method is shown to generalize better to novel objects and perform surprisingly well on out-of-distribution in-the-wild scenes of portable-sized objects. The resulting system allows us to predict descriptive affordance information, such as hand articulation and approaching orientation. Project page: https://judyye.github.io/affordiffusion-ww

Metal-Organic Framework-Derived Ni-S/C Catalysts for Selective Alkyne Hydrogenation

Acknowledgments This work was financially supported by the National Natural Science Foundation of China (22278017), the Fundamental Research Funds for the Central Universities (buctrc202303, JD2325), and the Young Elite Scientists Sponsorship Program by BAST (No. BYESS2023087).Peer reviewedPostprin

Understanding the Role of Coordinatively Unsaturated Al3+ Sites on Nanoshaped Al2O3 for Creating Uniform Ni–Cu Alloys for Selective Hydrogenation of Acetylene

Acknowledgments This work was financially supported by the National Key R&D Program of China (2021YFB3801600), the National Natural Science Foundation of China (22218017), and the Fundamental Research Funds for the Central Universities (buctrc201921, JD2223). We acknowledge the Beijing Synchrotron Radiation Facility (BSRF) for providing the experimental resources for XAS experiments.Peer reviewedPostprin

Chiral charge density wave and backscattering-immune orbital texture in monolayer 1T-TiTe2

Non-trivial electronic states are attracting intense attention in low-dimensional physics. Though chirality has been identified in charge states with a scalar order parameter, its intertwining with charge density waves (CDW), film thickness and the impact on the electronic behaviors remain less well understood. Here, using scanning tunneling microscopy, we report a 2 x 2 chiral CDW as well as a strong suppression of the Te-5p hole-band backscattering in monolayer 1T-TiTe2. These exotic characters vanish in bilayer TiTe2 with a non-CDW state. Theoretical calculations approve that chirality comes from a helical stacking of the triple-q CDW components and therefore can persist at the two-dimensional limit. Furthermore, the chirality renders the Te-5p bands an unconventional orbital texture that prohibits electron backscattering. Our study establishes TiTe2 as a promising playground for manipulating the chiral ground states at the monolayer limit and provides a novel path to engineer electronic properties from an orbital degree.Comment: 21 pages, 5 figure