Model Reduction for Large-Scale Systems with High Dimensional Parametric Input Space

A model-constrained adaptive sampling methodology is proposed for reduction of large-scale systems with high-dimensional parametric input spaces. Our model reduction method uses a reduced basis approach, which requires the computation of high-fidelity solutions at a number of sample points throughout the parametric input space. A key challenge that must be addressed in the optimization, control, and probabilistic settings is the need for the reduced models to capture variation over this parametric input space, which, for many applications, will be of high dimension. We pose the task of determining appropriate sample points as a PDE-constrained optimization problem, which is implemented using an efficient adaptive algorithm that scales well to systems with a large number of parameters. The methodology is demonstrated for examples with parametric input spaces of dimension 11 and 21, which describe thermal analysis and design of a heat conduction fin, and compared with statistically-based sampling methods. For this example, the model-constrained adaptive sampling leads to reduced models that, for a given basis size, have error several orders of magnitude smaller than that obtained using the other methods

LEGION: Harnessing Pre-trained Language Models for GitHub Topic Recommendations with Distribution-Balance Loss

Open-source development has revolutionized the software industry by promoting collaboration, transparency, and community-driven innovation. Today, a vast amount of various kinds of open-source software, which form networks of repositories, is often hosted on GitHub - a popular software development platform. To enhance the discoverability of the repository networks, i.e., groups of similar repositories, GitHub introduced repository topics in 2017 that enable users to more easily explore relevant projects by type, technology, and more. It is thus crucial to accurately assign topics for each GitHub repository. Current methods for automatic topic recommendation rely heavily on TF-IDF for encoding textual data, presenting challenges in understanding semantic nuances. This paper addresses the limitations of existing techniques by proposing Legion, a novel approach that leverages Pre-trained Language Models (PTMs) for recommending topics for GitHub repositories. The key novelty of Legion is three-fold. First, Legion leverages the extensive capabilities of PTMs in language understanding to capture contextual information and semantic meaning in GitHub repositories. Second, Legion overcomes the challenge of long-tailed distribution, which results in a bias toward popular topics in PTMs, by proposing a Distribution-Balanced Loss (DB Loss) to better train the PTMs. Third, Legion employs a filter to eliminate vague recommendations, thereby improving the precision of PTMs. Our empirical evaluation on a benchmark dataset of real-world GitHub repositories shows that Legion can improve vanilla PTMs by up to 26% on recommending GitHubs topics. Legion also can suggest GitHub topics more precisely and effectively than the state-of-the-art baseline with an average improvement of 20% and 5% in terms of Precision and F1-score, respectively.Comment: Accepted to EASE'2

Computational chemo-thermo-mechanical coupling phase-field model for complex fracture induced by early-age shrinkage and hydration heat in cement-based materials

In this paper, we present a new multi-physics computational framework that enables us to capture and investigate complex fracture behavior in cement-based materials at early-age. The present model consists of coupling the most important chemo-thermo-mechanical processes to describe temperature evolution, variation of hydration degree, and mechanical behavior. The changes of material properties are expressed as a function of the hydration degree, to capture the age effects. Fracture analysis of these processes are then accommodated by a versatile phase field model in the framework of smeared crack models, addressing the influence of cracks on hydration and thermal transfer. We additionally describe a stable and robust numerical algorithm, which aims to solve coupled problems by using a staggered scheme. The developed approach is applied to study the fracture phenomena at both macroscopic and mesoscopic scales, in which all microstructural heterogeneities of sand and cement matrix are explicitly accounted. Nucleation, initiation, and propagation of complex crack network are simulated in an efficient way demonstrating the potential of the proposed approach to assess the early-age defects in concrete structures and materials

Role of interfacial transition zone in phase field modeling of fracture in layered heterogeneous structures

Mechanical behavior of layered materials and structures greatly depends on the mechanical behavior of interfaces. In the past decades, the failure in such layered media has been studied by many researchers due to their critical role in the mechanics and physics of solids. This study aims at investigating crack-interface interaction in two-dimensional (2-D) and three-dimensional (3-D) layered media by a phase field model. Our objectives are fourfold: (a) to better understand fracture behavior in layered heterogeneous systems under quasi-static load; (b) to introduce a new methodology for better describing interfaces by a regularized interfacial transition zone in the context of varia-tional phase field approach, exploring its important role; (c) to show the accuracy , performance and applicability of the present model in modeling material failure at the interfaces in both 2-D and 3-D bodies; and (d) to quantitatively validate computed crack path with respect to experimental data. Phase field models with both perfectly and cohesive bonded interfaces are thus derived. A regularized interfacial transition zone is introduced to capture characteristics of material mismatch at the interfaces. Numerical examples for 2-D and 3-D layered systems with experimental validation provide fundamentals of fracture behavior in layered structures. The obtained results shed light on the behavior of crack paths, which are drastically affected by the elastic modulus mismatch between two layers and interface types, and reveal the important role of the proposed interfacial transition zone in phase field modeling of crack interface interactions

Identification of nonlinear heat transfer laws from boundary observations

We consider the problem of identifying a nonlinear heat transfer law at the boundary, or of the temperature-dependent heat transfer coefficient in a parabolic equation from boundary observations. As a practical example, this model applies to the heat transfer coefficient that describes the intensity of heat exchange between a hot wire and the cooling water in which it is placed. We reformulate the inverse problem as a variational one which aims to minimize a misfit functional and prove that it has a solution. We provide a gradient formula for the misfit functional and then use some iterative methods for solving the variational problem. Thorough investigations are made with respect to several initial guesses and amounts of noise in the input data. Numerical results show that the methods are robust, stable and accurate

AGV Trajectory Control Based on Laser Sensor Navigation

Autonomous Guided Vehicle Systems (AGVs) are used to transport goods and products in manufacturing fields where navigation can be done in a structured environment. In order to track the given trajectory, a tracking control based on Lyapunov stability theory is introduced. The use of the nonlinear Lyapunov technique provides robustness for load disturbance and sensor noise. To apply Lyapunov\u27s theorem, the kinematic model of AGV is given. To recognize its position in indoor environment, in this paper, a laser sensor device NAV200 is used to detect the AGV position in real-time. For simulation and experiment, software and hardware are described. The AGV consists of 4 wheels with two passive wheels and two driving wheels. A controller is developed based on industrial computer. The effectiveness of the proposed controller is proved by simulation and experimental results.[AGV Trajectory Control, Laser Sensor Navigation

Satellites May Underestimate Rice Residue and Associated Burning Emissions in Vietnam

In this study, we estimate rice residue, associated burning emissions, and compare results with existing emissions inventories employing a bottom-up approach. We first estimated field-level post-harvest rice residues, including separate fuel-loading factors for rice straw and rice stubble. Results suggested fuel-loading factors of 0.27 kg/sq m (+/-0.033), 0.61 kg/sq m (+/-0.076), and 0.88 kg/sq m (+/-0.083) for rice straw, stubble, and total post-harvest biomass, respectively. Using these factors, we quantified potential emissions from rice residue burning and compared our estimates with other studies. Our results suggest total rice residue burning emissions as 2.24 Gg PM2.5, 36.54 Gg CO and 567.79 Gg CO2 for Hanoi Province, which are significantly higher than earlier studies. We attribute our higher emission estimates to improved fuel-loading factors; moreover, we infer that some earlier studies relying on residue-to-product ratios could be underestimating rice residue emissions by more than a factor of 2.3 for Hanoi, Vietnam. Using the rice planted area data from the Vietnamese government, and combining our fuel-loading factors, we also estimated rice residue PM2.5 emissions for the entirety of Vietnam and compared these estimates with an existing all-sources emissions inventory, and the Global Fire Emissions Database (GFED). Results suggest 75.98 Gg of PM2.5 released from rice residue burning accounting for 12.8% of total emissions for Vietnam. The GFED database suggests 42.56 Gg PM2.5 from biomass burning with 5.62 Gg attributed to agricultural waste burning indicating satellite-based methods may be significantly underestimating emissions. Our results not only provide improved residue and emission estimates, but also highlight the need for emissions mitigation from rice residue burning

Iisy: hybrid in-network classification using programmable switches

The soaring use of machine learning leads to increasing processing demands. As data volume keeps growing, providing classification services with good machine learning performance, high throughput, low latency, and minimal equipment overheads becomes a challenge. Offloading machine learning tasks to network switches can be a scalable solution to this problem, providing high throughput and low latency. However, network devices are resource constrained, and lack support for machine learning functionality. In this paper, we introduce IIsy - a novel mapping tool of machine learning classification models to off-the-shelf switches. Using an efficient encoding algorithm, IIsy enables fitting a range of classification models on switches, coexisting with standard switch functionality. To overcome resource constraints, IIsy adopts a hybrid approach for ensemble models, running a small model on a switch and a large model on the backend. The evaluation shows that IIsy achieves near-optimal classification results, within minimum resource overheads, and while reducing the load on the backend by 70% for data-intensive use cases

Comparison of some Reduced Representation Approximations

In the field of numerical approximation, specialists considering highly complex problems have recently proposed various ways to simplify their underlying problems. In this field, depending on the problem they were tackling and the community that are at work, different approaches have been developed with some success and have even gained some maturity, the applications can now be applied to information analysis or for numerical simulation of PDE's. At this point, a crossed analysis and effort for understanding the similarities and the differences between these approaches that found their starting points in different backgrounds is of interest. It is the purpose of this paper to contribute to this effort by comparing some constructive reduced representations of complex functions. We present here in full details the Adaptive Cross Approximation (ACA) and the Empirical Interpolation Method (EIM) together with other approaches that enter in the same category