Data infrastructure plays a vital role as the cornerstone supporting the operations of modern society. Nevertheless, data characterization methods within this infrastructure encounter challenges related to inefficiency, high costs, and the management of fuzzy and complex retrieval requirements. To tackle these obstacles, this paper introduces a highly efficient approach for characterizing digital objects called DOR-RAF. This methodology integrates Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) to enhance the intelligent retrieval experience for users. It presents an interactive retrieval mechanism that accurately identifies users’ true needs through iterative dialogues, effectively handling user queries despite their initial vagueness. The experimental results show that DOR-RAF outperforms the traditional metadata-based retrieval methods in terms of F1 Score and other metrics on the task of retrieving digital objects in a data infrastructure scenario. Meanwhile, the experiments show that the development of data characterisation methods using RAG technology has a broad prospect, and confirm that DOR-RAF outperforms traditional RAG methods in terms of Context Precision, Answer Correctness, etc. in the task of data characterisation.

In physically based-based animation, pure particle methods are popular due to their simple data structure, easy implementation, and convenient parallelization. As a pure particle-based method and using Galerkin discretization, the Moving Least Square Reproducing Kernel Method (MLSRK) was developed in engineering computation as a general numerical tool for solving PDEs. The basic idea of Moving Least Square (MLS) has also been used in computer graphics to estimate deformation gradient for deformable solids. Based on these previous studies, we propose a multiphase MLSRK framework that animates complex and coupled fluids and solids in a unified manner. Specifically, we use the Cauchy momentum equation and phase field model to uniformly capture the momentum balance and phase evolution/interaction in a multiphase system, and systematically formulate the MLSRK discretization to support general multiphase constitutive models. A series of animation examples are presented to demonstrate the performance of our new multiphase MLSRK framework, including hyperelastic, elastoplastic, viscous, fracturing and multiphase coupling behaviours etc.

This work extends existing multiphase-fluid SPH frameworks to cover solid phases, including deformable bodies and granular materials. In our extended multiphase SPH framework, the distribution and shapes of all phases, both fluids and solids, are uniformly represented by their volume fraction functions. The dynamics of the multiphase system is governed by conservation of mass and momentum within different phases. The behavior of individual phases and the interactions between them are represented by corresponding constitutive laws, which are functions of the volume fraction fields and the velocity fields. Our generalized multiphase SPH framework does not require separate equations for specific phases or tedious interface tracking. As the distribution, shape and motion of each phase is represented and resolved in the same way, the proposed approach is robust, efficient and easy to implement. Various simulation results are presented to demonstrate the capabilities of our new multiphase SPH framework, including deformable bodies, granular materials, interaction between multiple fluids and deformable solids, flow in porous media, and dissolution of deformable solids.

This article presents a novel and flexible bubble modelling technique for multi-fluid simulations using a volume fraction representation. By combining the volume fraction data obtained from a primary multi-fluid simulation with simple and efficient secondary bubble simulation, a range of real-world bubble phenomena are captured with a high degree of physical realism, including large bubble deformation, sub-cell bubble motion, bubble stacking over the liquid surface, bubble volume change, dissolving of bubbles, etc. Without any change in the primary multi-fluid simulator, our bubble modelling approach is applicable to any multi-fluid simulator based on the volume fraction representation.