arXiv - CS - Computational Engineering, Finance, and Science最新文献

{"title":"Exploring Commercial Vehicle Detouring Patterns through the Application of Probe Trajectory Data","authors":"Mark Franz PhD, Sara Zahedian PhD, Dhairya Parekh, Tahsin Emtenam PhD, Greg Jordan","doi":"arxiv-2407.17319","DOIUrl":"https://doi.org/arxiv-2407.17319","url":null,"abstract":"Understanding motorist detouring behavior is critical for both traffic\u0000operations and planning applications. However, measuring real-world detouring\u0000behavior is challenging due to the need to track the movement of individual\u0000vehicles. Recent developments in high-resolution vehicle trajectory data have\u0000enabled transportation professionals to observe real-world detouring behaviors\u0000without the need to install and maintain hardware such as license plate reading\u0000cameras. This paper investigates the feasibility of vehicle probe trajectory\u0000data to capture commercial motor vehicle (CMV) detouring behavior under three\u0000unique case studies. Before doing so, a validation analysis was conducted to\u0000investigate the ability of CMV probe trajectory data to represent overall CMV\u0000volumes at well-calibrated count stations near virtual weigh stations (VWS) in\u0000Maryland. The validation analysis showed strong positive correlations (above\u00000.75) at all VWS stations. Upon validating the data, a methodology was applied\u0000to assess CMV detour behaviors associated with CMV enforcement activities,\u0000congestion avoidance, and incident induced temporary road closures.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771818","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"KnowTD-An Actionable Knowledge Representation System for Thermodynamics","authors":"Luisa Vollmer, Sophie Fellenz, Fabian Jirasek, Heike Leitte, Hans Hasse","doi":"arxiv-2407.17169","DOIUrl":"https://doi.org/arxiv-2407.17169","url":null,"abstract":"We demonstrate that thermodynamic knowledge acquired by humans can be\u0000transferred to computers so that the machine can use it to solve thermodynamic\u0000problems and produce explainable solutions with a guarantee of correctness. The\u0000actionable knowledge representation system that we have created for this\u0000purpose is called KnowTD. It is based on an ontology of thermodynamics that\u0000represents knowledge of thermodynamic theory, material properties, and\u0000thermodynamic problems. The ontology is coupled with a reasoner that sets up\u0000the problem to be solved based on user input, extracts the correct, pertinent\u0000equations from the ontology, solves the resulting mathematical problem, and\u0000returns the solution to the user, together with an explanation of how it was\u0000obtained. KnowTD is presently limited to simple thermodynamic problems, similar\u0000to those discussed in an introductory course in Engineering Thermodynamics.\u0000This covers the basic theory and working principles of thermodynamics. KnowTD\u0000is designed in a modular way and is easily extendable.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771819","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"COEFF-KANs: A Paradigm to Address the Electrolyte Field with KANs","authors":"Xinhe Li, Zhuoying Feng, Yezeng Chen, Weichen Dai, Zixu He, Yi Zhou, Shuhong Jiao","doi":"arxiv-2407.20265","DOIUrl":"https://doi.org/arxiv-2407.20265","url":null,"abstract":"To reduce the experimental validation workload for chemical researchers and\u0000accelerate the design and optimization of high-energy-density lithium metal\u0000batteries, we aim to leverage models to automatically predict Coulombic\u0000Efficiency (CE) based on the composition of liquid electrolytes. There are\u0000mainly two representative paradigms in existing methods: machine learning and\u0000deep learning. However, the former requires intelligent input feature selection\u0000and reliable computational methods, leading to error propagation from feature\u0000estimation to model prediction, while the latter (e.g. MultiModal-MoLFormer)\u0000faces challenges of poor predictive performance and overfitting due to limited\u0000diversity in augmented data. To tackle these issues, we propose a novel method\u0000COEFF (COlumbic EFficiency prediction via Fine-tuned models), which consists of\u0000two stages: pre-training a chemical general model and fine-tuning on downstream\u0000domain data. Firstly, we adopt the publicly available MoLFormer model to obtain\u0000feature vectors for each solvent and salt in the electrolyte. Then, we perform\u0000a weighted average of embeddings for each token across all molecules, with\u0000weights determined by the respective electrolyte component ratios. Finally, we\u0000input the obtained electrolyte features into a Multi-layer Perceptron or\u0000Kolmogorov-Arnold Network to predict CE. Experimental results on a real-world\u0000dataset demonstrate that our method achieves SOTA for predicting CE compared to\u0000all baselines. Data and code used in this work will be made publicly available\u0000after the paper is published.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141863337","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Fusing LLMs and KGs for Formal Causal Reasoning behind Financial Risk Contagion","authors":"Guanyuan Yu, Xv Wang, Qing Li, Yu Zhao","doi":"arxiv-2407.17190","DOIUrl":"https://doi.org/arxiv-2407.17190","url":null,"abstract":"Financial risks trend to spread from one entity to another, ultimately\u0000leading to systemic risks. The key to preventing such risks lies in\u0000understanding the causal chains behind risk contagion. Despite this, prevailing\u0000approaches primarily emphasize identifying risks, overlooking the underlying\u0000causal analysis of risk. To address such an issue, we propose a Risk Contagion\u0000Causal Reasoning model called RC2R, which uses the logical reasoning\u0000capabilities of large language models (LLMs) to dissect the causal mechanisms\u0000of risk contagion grounded in the factual and expert knowledge embedded within\u0000financial knowledge graphs (KGs). At the data level, we utilize financial KGs\u0000to construct causal instructions, empowering LLMs to perform formal causal\u0000reasoning on risk propagation and tackle the \"causal parrot\" problem of LLMs.\u0000In terms of model architecture, we integrate a fusion module that aligns tokens\u0000and nodes across various granularities via multi-scale contrastive learning,\u0000followed by the amalgamation of textual and graph-structured data through soft\u0000prompt with cross multi-head attention mechanisms. To quantify risk contagion,\u0000we introduce a risk pathway inference module for calculating risk scores for\u0000each node in the graph. Finally, we visualize the risk contagion pathways and\u0000their intensities using Sankey diagrams, providing detailed causal\u0000explanations. Comprehensive experiments on financial KGs and supply chain\u0000datasets demonstrate that our model outperforms several state-of-the-art models\u0000in prediction performance and out-of-distribution (OOD) generalization\u0000capabilities. We will make our dataset and code publicly accessible to\u0000encourage further research and development in this field.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Reduced Order Model conditioned on monitoring features for estimation and uncertainty quantification in engineered systems","authors":"Konstantinos Vlachas, Thomas Simpson, Anthony Garland, D. Dane Quinn, Charbel Farhat, Eleni Chatzi","doi":"arxiv-2407.17139","DOIUrl":"https://doi.org/arxiv-2407.17139","url":null,"abstract":"Reduced Order Models (ROMs) form essential tools across engineering domains\u0000by virtue of their function as surrogates for computationally intensive digital\u0000twinning simulators. Although purely data-driven methods are available for ROM\u0000construction, schemes that allow to retain a portion of the physics tend to\u0000enhance the interpretability and generalization of ROMs. However, physics-based\u0000techniques can adversely scale when dealing with nonlinear systems that feature\u0000parametric dependencies. This study introduces a generative physics-based ROM\u0000that is suited for nonlinear systems with parametric dependencies and is\u0000additionally able to quantify the confidence associated with the respective\u0000estimates. A main contribution of this work is the conditioning of these\u0000parametric ROMs to features that can be derived from monitoring measurements,\u0000feasibly in an online fashion. This is contrary to most existing ROM schemes,\u0000which remain restricted to the prescription of the physics-based, and usually a\u0000priori unknown, system parameters. Our work utilizes conditional Variational\u0000Autoencoders to continuously map the required reduction bases to a feature\u0000vector extracted from limited output measurements, while additionally allowing\u0000for a probabilistic assessment of the ROM-estimated Quantities of Interest. An\u0000auxiliary task using a neural network-based parametrization of suitable\u0000probability distributions is introduced to re-establish the link with physical\u0000model parameters. We verify the proposed scheme on a series of simulated case\u0000studies incorporating effects of geometric and material nonlinearity under\u0000parametric dependencies related to system properties and input load\u0000characteristics.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771901","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties","authors":"Rasoul Najafi Koopas, Shahed Rezaei, Natalie Rauter, Richard Ostwald, Rolf Lammering","doi":"arxiv-2407.15665","DOIUrl":"https://doi.org/arxiv-2407.15665","url":null,"abstract":"A spatiotemporal deep learning framework is proposed that is capable of 2D\u0000full-field prediction of fracture in concrete mesostructures. This framework\u0000not only predicts fractures but also captures the entire history of the\u0000fracture process, from the crack initiation in the interfacial transition zone\u0000to the subsequent propagation of the cracks in the mortar matrix. In addition,\u0000a convolutional neural network is developed which can predict the averaged\u0000stress-strain curve of the mesostructures. The UNet modeling framework, which\u0000comprises an encoder-decoder section with skip connections, is used as the deep\u0000learning surrogate model. Training and test data are generated from\u0000high-fidelity fracture simulations of randomly generated concrete\u0000mesostructures. These mesostructures include geometric variabilities such as\u0000different aggregate particle geometrical features, spatial distribution, and\u0000the total volume fraction of aggregates. The fracture simulations are carried\u0000out in Abaqus, utilizing the cohesive phase-field fracture modeling technique\u0000as the fracture modeling approach. In this work, to reduce the number of\u0000training datasets, the spatial distribution of three sets of material\u0000properties for three-phase concrete mesostructures, along with the spatial\u0000phase-field damage index, are fed to the UNet to predict the corresponding\u0000stress and spatial damage index at the subsequent step. It is shown that after\u0000the training process using this methodology, the UNet model is capable of\u0000accurately predicting damage on the unseen test dataset by using 470 datasets.\u0000Moreover, another novel aspect of this work is the conversion of irregular\u0000finite element data into regular grids using a developed pipeline. This\u0000approach allows for the implementation of less complex UNet architecture and\u0000facilitates the integration of phase-field fracture equations into surrogate\u0000models for future developments.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771826","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Spatio-Temporal Approach with Self-Corrective Causal Inference for Flight Delay Prediction","authors":"Qihui Zhu, Shenwen Chen, Tong Guo, Yisheng Lv, Wenbo Du","doi":"arxiv-2407.15185","DOIUrl":"https://doi.org/arxiv-2407.15185","url":null,"abstract":"Accurate flight delay prediction is crucial for the secure and effective\u0000operation of the air traffic system. Recent advances in modeling inter-airport\u0000relationships present a promising approach for investigating flight delay\u0000prediction from the multi-airport scenario. However, the previous prediction\u0000works only accounted for the simplistic relationships such as traffic flow or\u0000geographical distance, overlooking the intricate interactions among airports\u0000and thus proving inadequate. In this paper, we leverage causal inference to\u0000precisely model inter-airport relationships and propose a self-corrective\u0000spatio-temporal graph neural network (named CausalNet) for flight delay\u0000prediction. Specifically, Granger causality inference coupled with a\u0000self-correction module is designed to construct causality graphs among airports\u0000and dynamically modify them based on the current airport's delays.\u0000Additionally, the features of the causality graphs are adaptively extracted and\u0000utilized to address the heterogeneity of airports. Extensive experiments are\u0000conducted on the real data of top-74 busiest airports in China. The results\u0000show that CausalNet is superior to baselines. Ablation studies emphasize the\u0000power of the proposed self-correction causality graph and the graph feature\u0000extraction module. All of these prove the effectiveness of the proposed\u0000methodology.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141785093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economy Watchers Survey provides Datasets and Tasks for Japanese Financial Domain","authors":"Masahiro Suzuki, Hiroki Sakaji","doi":"arxiv-2407.14727","DOIUrl":"https://doi.org/arxiv-2407.14727","url":null,"abstract":"Many natural language processing (NLP) tasks in English or general domains\u0000are widely available and are often used to evaluate pre-trained language\u0000models. In contrast, there are fewer tasks available for languages other than\u0000English and for the financial domain. In particular, tasks in Japanese and the\u0000financial domain are limited. We construct two large datasets using materials\u0000published by a Japanese central government agency. The datasets provide three\u0000Japanese financial NLP tasks, which include a 3-class and 12-class\u0000classification for categorizing sentences, as well as a 5-class classification\u0000task for sentiment analysis. Our datasets are designed to be comprehensive and\u0000up-to-date, leveraging an automatic update framework that ensures the latest\u0000task datasets are publicly available anytime.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771898","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating lane-change prediction into energy-efficient speed control of connected autonomous vehicles at intersections","authors":"Maziar Zamanpour, Suiyi He, Michael W. Levin, Zongxuan Sun","doi":"arxiv-2407.15004","DOIUrl":"https://doi.org/arxiv-2407.15004","url":null,"abstract":"Connected and autonomous vehicles (CAVs) possess the capability of perception\u0000and information broadcasting with other CAVs and connected intersections.\u0000Additionally, they exhibit computational abilities and can be controlled\u0000strategically, offering energy benefits. One potential control strategy is\u0000real-time speed control, which adjusts the vehicle speed by taking advantage of\u0000broadcasted traffic information, such as signal timings. However, the optimal\u0000control is likely to increase the gap in front of the controlled CAV, which\u0000induces lane changing by other drivers. This study proposes a modified traffic\u0000flow model that aims to predict lane-changing occurrences and assess the impact\u0000of lane changes on future traffic states. The primary objective is to improve\u0000energy efficiency. The prediction model is based on a cell division platform\u0000and is derived considering the additional flow during lane changing. An optimal\u0000control strategy is then developed, subject to the predicted trajectory\u0000generated for the preceding vehicle. Lane change prediction estimates future\u0000speed and gap of vehicles, based on predicted traffic states. The proposed\u0000framework outperforms the non-lane change traffic model, resulting in up to 13%\u0000energy savings when lane changing is predicted 4-6 seconds in advance.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141771821","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Projection-based model-order reduction for unstructured meshes with graph autoencoders","authors":"Liam K. MagargalDepartment of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, United States, Parisa KhodabakhshiDepartment of Mechanical Engineering and Mechanics, Lehigh University, Bethlehem, PA, United States, Steven N. RodriguezComputational Multiphysics Systems Laboratory, United States Naval Research Laboratory, Washington, DC, United States, Justin W. JaworskiKevin T. Crofton Department of Aerospace and Ocean Engineering, Virginia Tech, Blacksburg, VA, United States, John G. MichopoulosComputational Multiphysics Systems Laboratory, United States Naval Research Laboratory, Washington, DC, United States","doi":"arxiv-2407.13669","DOIUrl":"https://doi.org/arxiv-2407.13669","url":null,"abstract":"This paper presents a graph autoencoder architecture capable of performing\u0000projection-based model-order reduction (PMOR) on advection-dominated flows\u0000modeled by unstructured meshes. The autoencoder is coupled with the time\u0000integration scheme from a traditional deep least-squares Petrov-Galerkin\u0000projection and provides the first deployment of a graph autoencoder into a PMOR\u0000framework. The presented graph autoencoder is constructed with a two-part\u0000process that consists of (1) generating a hierarchy of reduced graphs to\u0000emulate the compressive abilities of convolutional neural networks (CNNs) and\u0000(2) training a message passing operation at each step in the hierarchy of\u0000reduced graphs to emulate the filtering process of a CNN. The resulting\u0000framework provides improved flexibility over traditional CNN-based autoencoders\u0000because it is extendable to unstructured meshes. To highlight the capabilities\u0000of the proposed framework, which is named geometric deep least-squares\u0000Petrov-Galerkin (GD-LSPG), we benchmark the method on a one-dimensional\u0000Burgers' equation problem with a structured mesh and demonstrate the\u0000flexibility of GD-LSPG by deploying it to a two-dimensional Euler equations\u0000model that uses an unstructured mesh. The proposed framework provides\u0000considerable improvement in accuracy for very low-dimensional latent spaces in\u0000comparison with traditional affine projections.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141745039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}