arXiv - CS - Computational Engineering, Finance, and Science最新文献_第7页

Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space 通过声学响应编码潜空间变异自动编码器反向设计非参数化通风声学谐振器

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-12 DOI: arxiv-2408.05917

Min Woo Cho, Seok Hyeon Hwang, Jun-Young Jang, Jin Yeong Song, Sun-kwang Hwang, Kyoung Je Cha, Dong Yong Park, Kyungjun Song, Sang Min Park

{"title":"Inverse design of Non-parameterized Ventilated Acoustic Resonator via Variational Autoencoder with Acoustic Response-encoded Latent Space","authors":"Min Woo Cho, Seok Hyeon Hwang, Jun-Young Jang, Jin Yeong Song, Sun-kwang Hwang, Kyoung Je Cha, Dong Yong Park, Kyungjun Song, Sang Min Park","doi":"arxiv-2408.05917","DOIUrl":"https://doi.org/arxiv-2408.05917","url":null,"abstract":"Ventilated acoustic resonator(VAR), a type of acoustic metamaterial, emerge\u0000as an alternative for sound attenuation in environments that require\u0000ventilation, owing to its excellent low-frequency attenuation performance and\u0000flexible shape adaptability. However, due to the non-linear acoustic responses\u0000of VARs, the VAR designs are generally obtained within a limited parametrized\u0000design space, and the design relies on the iteration of the numerical\u0000simulation which consumes a considerable amount of computational time and\u0000resources. This paper proposes an acoustic response-encoded variational\u0000autoencoder (AR-VAE), a novel variational autoencoder-based generative design\u0000model for the efficient and accurate inverse design of VAR even with\u0000non-parametrized designs. The AR-VAE matches the high-dimensional acoustic\u0000response with the VAR cross-section image in the dimension-reduced latent\u0000space, which enables the AR-VAE to generate various non-parametrized VAR\u0000cross-section images with the target acoustic response. AR-VAE generates\u0000non-parameterized VARs from target acoustic responses, which show a 25-fold\u0000reduction in mean squared error compared to conventional deep learning-based\u0000parameter searching methods while exhibiting lower average mean squared error\u0000and peak frequency variance. By combining the inverse-designed VARs by AR-VAE,\u0000multi-cavity VAR was devised for broadband and multitarget peak frequency\u0000attenuation. The proposed design method presents a new approach for structural\u0000inverse-design with a high-dimensional non-linear physical response.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142227901","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}

引用次数: 0

HyperCAN: Hypernetwork-Driven Deep Parameterized Constitutive Models for Metamaterials HyperCAN：超网络驱动的超材料深度参数化构造模型

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-12 DOI: arxiv-2408.06017

Li Zheng, Dennis M. Kochmann, Siddhant Kumar

{"title":"HyperCAN: Hypernetwork-Driven Deep Parameterized Constitutive Models for Metamaterials","authors":"Li Zheng, Dennis M. Kochmann, Siddhant Kumar","doi":"arxiv-2408.06017","DOIUrl":"https://doi.org/arxiv-2408.06017","url":null,"abstract":"We introduce HyperCAN, a machine learning framework that utilizes\u0000hypernetworks to construct adaptable constitutive artificial neural networks\u0000for a wide range of beam-based metamaterials exhibiting diverse mechanical\u0000behavior under finite deformations. HyperCAN integrates an input convex network\u0000that models the nonlinear stress-strain map of a truss lattice, while ensuring\u0000adherence to fundamental mechanics principles, along with a hypernetwork that\u0000dynamically adjusts the parameters of the convex network as a function of the\u0000lattice topology and geometry. This unified framework demonstrates robust\u0000generalization in predicting the mechanical behavior of previously unseen\u0000metamaterial designs and loading scenarios well beyond the training domain. We\u0000show how HyperCAN can be integrated into multiscale simulations to accurately\u0000capture the highly nonlinear responses of large-scale truss metamaterials,\u0000closely matching fully resolved simulations while significantly reducing\u0000computational costs. This offers new efficient opportunities for the multiscale\u0000design and optimization of truss metamaterials.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"28 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211345","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}

引用次数: 0

An anisotropic, brittle damage model for finite strains with a generic damage tensor regularization 采用通用损伤张量正则化的各向异性有限应变脆性损伤模型

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-12 DOI: arxiv-2408.06140

Tim van der Velden, Stefanie Reese, Hagen Holthusen, Tim Brepols

{"title":"An anisotropic, brittle damage model for finite strains with a generic damage tensor regularization","authors":"Tim van der Velden, Stefanie Reese, Hagen Holthusen, Tim Brepols","doi":"arxiv-2408.06140","DOIUrl":"https://doi.org/arxiv-2408.06140","url":null,"abstract":"This paper establishes a universal framework for the nonlocal modeling of\u0000anisotropic damage at finite strains. By the combination of two recent works,\u0000the new framework allows for the flexible incorporation of different\u0000established hyperelastic finite strain material formulations into anisotropic\u0000damage whilst ensuring mesh-independent results by employing a generic set of\u0000micromorphic gradient-extensions. First, the anisotropic damage model,\u0000generally satisfying the damage growth criterion, is investigated for the\u0000specific choice of a Neo-Hookean material on a single element. Next, the model\u0000is applied with different gradient-extensions in structural simulations of an\u0000asymmetrically notched specimen to identify an efficient choice in the form of\u0000a volumetric-deviatoric regularization. Thereafter, the universal framework,\u0000which is without loss of generality here specified for a Neo-Hookean material\u0000with a volumetric-deviatoric gradient-extension, successfully serves for the\u0000complex simulation of a pressure loaded rotor blade. After acceptance of the manuscript, we make the codes of the material\u0000subroutines accessible to the public at\u0000https://doi.org/10.5281/zenodo.11171630.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211347","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}

引用次数: 0

Research on Heterogeneous Computation Resource Allocation based on Data-driven Method 基于数据驱动方法的异构计算资源分配研究

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-11 DOI: arxiv-2408.05671

Xirui Tang, Zeyu Wang, Xiaowei Cai, Honghua Su, Changsong Wei

{"title":"Research on Heterogeneous Computation Resource Allocation based on Data-driven Method","authors":"Xirui Tang, Zeyu Wang, Xiaowei Cai, Honghua Su, Changsong Wei","doi":"arxiv-2408.05671","DOIUrl":"https://doi.org/arxiv-2408.05671","url":null,"abstract":"The rapid development of the mobile Internet and the Internet of Things is\u0000leading to a diversification of user devices and the emergence of new mobile\u0000applications on a regular basis. Such applications include those that are\u0000computationally intensive, such as pattern recognition, interactive gaming,\u0000virtual reality, and augmented reality. However, the computing and energy\u0000resources available on the user's equipment are limited, which presents a\u0000challenge in effectively supporting such demanding applications. In this work,\u0000we propose a heterogeneous computing resource allocation model based on a\u0000data-driven approach. The model first collects and analyzes historical workload\u0000data at scale, extracts key features, and builds a detailed data set. Then, a\u0000data-driven deep neural network is used to predict future resource\u0000requirements. Based on the prediction results, the model adopts a dynamic\u0000adjustment and optimization resource allocation strategy. This strategy not\u0000only fully considers the characteristics of different computing resources, but\u0000also accurately matches the requirements of various tasks, and realizes dynamic\u0000and flexible resource allocation, thereby greatly improving the overall\u0000performance and resource utilization of the system. Experimental results show\u0000that the proposed method is significantly better than the traditional resource\u0000allocation method in a variety of scenarios, demonstrating its excellent\u0000accuracy and adaptability.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211346","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}

引用次数: 0

A reduced simulation applied to viscoelastic fatigue of polymers using a time multi-scale approach based on Partition of Unity method 使用基于统一分割法的时间多尺度方法，对聚合物的粘弹性疲劳进行简化模拟

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-09 DOI: arxiv-2408.05143

Sebastian Rodriguez, Angelo Pasquale, Jad Mounayer, Diego Canales, Marianne Beringhier, Chady Ghnatios, Amine Ammar, Francisco Chinesta

引用次数: 0

FiST-Financial Style Transfer with Hallucination and Creativity Control Framework FiST--金融风格转移与幻觉和创造力控制框架

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-09 DOI: arxiv-2408.05365

Sohini Roychowdhury, Marko Krema, Brian Moore, Xingjian Lai, Dike Effedua, Bharat Jethwani

引用次数: 0

Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing 基于机器学习的激光增材制造原位监测视听跨模态知识转移

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-09 DOI: arxiv-2408.05307

Jiarui Xie, Mutahar Safdar, Lequn Chen, Seung Ki Moon, Yaoyao Fiona Zhao

{"title":"Audio-visual cross-modality knowledge transfer for machine learning-based in-situ monitoring in laser additive manufacturing","authors":"Jiarui Xie, Mutahar Safdar, Lequn Chen, Seung Ki Moon, Yaoyao Fiona Zhao","doi":"arxiv-2408.05307","DOIUrl":"https://doi.org/arxiv-2408.05307","url":null,"abstract":"Various machine learning (ML)-based in-situ monitoring systems have been\u0000developed to detect laser additive manufacturing (LAM) process anomalies and\u0000defects. Multimodal fusion can improve in-situ monitoring performance by\u0000acquiring and integrating data from multiple modalities, including visual and\u0000audio data. However, multimodal fusion employs multiple sensors of different\u0000types, which leads to higher hardware, computational, and operational costs.\u0000This paper proposes a cross-modality knowledge transfer (CMKT) methodology that\u0000transfers knowledge from a source to a target modality for LAM in-situ\u0000monitoring. CMKT enhances the usefulness of the features extracted from the\u0000target modality during the training phase and removes the sensors of the source\u0000modality during the prediction phase. This paper proposes three CMKT methods:\u0000semantic alignment, fully supervised mapping, and semi-supervised mapping.\u0000Semantic alignment establishes a shared encoded space between modalities to\u0000facilitate knowledge transfer. It utilizes a semantic alignment loss to align\u0000the distributions of the same classes (e.g., visual defective and audio\u0000defective classes) and a separation loss to separate the distributions of\u0000different classes (e.g., visual defective and audio defect-free classes). The\u0000two mapping methods transfer knowledge by deriving the features of one modality\u0000from the other modality using fully supervised and semi-supervised learning.\u0000The proposed CMKT methods were implemented and compared with multimodal\u0000audio-visual fusion in an LAM in-situ anomaly detection case study. The\u0000semantic alignment method achieves a 98.4% accuracy while removing the audio\u0000modality during the prediction phase, which is comparable to the accuracy of\u0000multimodal fusion (98.2%).","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211348","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}

引用次数: 0

The impact of internal variability on benchmarking deep learning climate emulators 内部变异对深度学习气候模拟器基准测试的影响

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-09 DOI: arxiv-2408.05288

Björn Lütjens, Raffaele Ferrari, Duncan Watson-Parris, Noelle Selin

{"title":"The impact of internal variability on benchmarking deep learning climate emulators","authors":"Björn Lütjens, Raffaele Ferrari, Duncan Watson-Parris, Noelle Selin","doi":"arxiv-2408.05288","DOIUrl":"https://doi.org/arxiv-2408.05288","url":null,"abstract":"Full-complexity Earth system models (ESMs) are computationally very\u0000expensive, limiting their use in exploring the climate outcomes of multiple\u0000emission pathways. More efficient emulators that approximate ESMs can directly\u0000map emissions onto climate outcomes, and benchmarks are being used to evaluate\u0000their accuracy on standardized tasks and datasets. We investigate a popular\u0000benchmark in data-driven climate emulation, ClimateBench, on which deep\u0000learning-based emulators are currently achieving the best performance. We\u0000implement a linear regression-based emulator, akin to pattern scaling, and find\u0000that it outperforms the incumbent 100M-parameter deep learning foundation\u0000model, ClimaX, on 3 out of 4 regionally-resolved surface-level climate\u0000variables. While emulating surface temperature is expected to be predominantly\u0000linear, this result is surprising for emulating precipitation. We identify that\u0000this outcome is a result of high levels of internal variability in the\u0000benchmark targets. To address internal variability, we update the benchmark\u0000targets with ensemble averages from the MPI-ESM1.2-LR model that contain 50\u0000instead of 3 climate simulations per emission pathway. Using the new targets,\u0000we show that linear pattern scaling continues to be more accurate on\u0000temperature, but can be outperformed by a deep learning-based model for\u0000emulating precipitation. We publish our code, data, and an interactive tutorial\u0000at github.com/blutjens/climate-emulator.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142211349","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}

引用次数: 0

The LATIN-PGD methodology to nonlinear dynamics and quasi-brittle materials for future earthquake engineering applications 针对非线性动力学和准脆性材料的 LATIN-PGD 方法在未来地震工程中的应用

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-09 DOI: arxiv-2408.05108

Sebastian Rodriguez, Pierre-Etienne Charbonnel, Pierre Ladevèze, David Néron

引用次数: 0

Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic 混合交通中自动驾驶车辆变道的人机反馈强化学习

arXiv - CS - Computational Engineering, Finance, and Science Pub Date : 2024-08-08 DOI: arxiv-2408.04447

Yuting Wang, Lu Liu, Maonan Wang, Xi Xiong

{"title":"Reinforcement Learning from Human Feedback for Lane Changing of Autonomous Vehicles in Mixed Traffic","authors":"Yuting Wang, Lu Liu, Maonan Wang, Xi Xiong","doi":"arxiv-2408.04447","DOIUrl":"https://doi.org/arxiv-2408.04447","url":null,"abstract":"The burgeoning field of autonomous driving necessitates the seamless\u0000integration of autonomous vehicles (AVs) with human-driven vehicles, calling\u0000for more predictable AV behavior and enhanced interaction with human drivers.\u0000Human-like driving, particularly during lane-changing maneuvers on highways, is\u0000a critical area of research due to its significant impact on safety and traffic\u0000flow. Traditional rule-based decision-making approaches often fail to\u0000encapsulate the nuanced boundaries of human behavior in diverse driving\u0000scenarios, while crafting reward functions for learning-based methods\u0000introduces its own set of complexities. This study investigates the application\u0000of Reinforcement Learning from Human Feedback (RLHF) to emulate human-like\u0000lane-changing decisions in AVs. An initial RL policy is pre-trained to ensure\u0000safe lane changes. Subsequently, this policy is employed to gather data, which\u0000is then annotated by humans to train a reward model that discerns lane changes\u0000aligning with human preferences. This human-informed reward model supersedes\u0000the original, guiding the refinement of the policy to reflect human-like\u0000preferences. The effectiveness of RLHF in producing human-like lane changes is\u0000demonstrated through the development and evaluation of conservative and\u0000aggressive lane-changing models within obstacle-rich environments and mixed\u0000autonomy traffic scenarios. The experimental outcomes underscore the potential\u0000of RLHF to diversify lane-changing behaviors in AVs, suggesting its viability\u0000for enhancing the integration of AVs into the fabric of human-driven traffic.","PeriodicalId":501309,"journal":{"name":"arXiv - CS - Computational Engineering, Finance, and Science","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-08-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141936374","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}

引用次数: 0