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Physics Informed Machine Learning for Chemistry Tabulation 物理通知化学制表机器学习
J. Comput. Sci. Pub Date : 2022-11-06 DOI: 10.48550/arXiv.2211.03022
A. Salunkhe, Dwyer Deighan, P. DesJardin, V. Chandola
{"title":"Physics Informed Machine Learning for Chemistry Tabulation","authors":"A. Salunkhe, Dwyer Deighan, P. DesJardin, V. Chandola","doi":"10.48550/arXiv.2211.03022","DOIUrl":"https://doi.org/10.48550/arXiv.2211.03022","url":null,"abstract":"Modeling of turbulent combustion system requires modeling the underlying chemistry and the turbulent flow. Solving both systems simultaneously is computationally prohibitive. Instead, given the difference in scales at which the two sub-systems evolve, the two sub-systems are typically (re)solved separately. Popular approaches such as the Flamelet Generated Manifolds (FGM) use a two-step strategy where the governing reaction kinetics are pre-computed and mapped to a low-dimensional manifold, characterized by a few reaction progress variables (model reduction) and the manifold is then ``looked-up'' during the runtime to estimate the high-dimensional system state by the flow system. While existing works have focused on these two steps independently, in this work we show that joint learning of the progress variables and the look--up model, can yield more accurate results. We build on the base formulation and implementation ChemTab to include the dynamically generated Themochemical State Variables (Lower Dimensional Dynamic Source Terms). We discuss the challenges in the implementation of this deep neural network architecture and experimentally demonstrate it's superior performance.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"35 1","pages":"102001"},"PeriodicalIF":0.0,"publicationDate":"2022-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73563104","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: 1
Impact Learning: A Learning Method from Features Impact and Competition 影响学习:一种基于特征、影响和竞争的学习方法
J. Comput. Sci. Pub Date : 2022-11-04 DOI: 10.48550/arXiv.2211.02263
Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid, Masud Rana, M. Kowsher, Apurba Adhikary, S. Biswas, A. Bairagi
{"title":"Impact Learning: A Learning Method from Features Impact and Competition","authors":"Nusrat Jahan Prottasha, Saydul Akbar Murad, Abu Jafar Md Muzahid, Masud Rana, M. Kowsher, Apurba Adhikary, S. Biswas, A. Bairagi","doi":"10.48550/arXiv.2211.02263","DOIUrl":"https://doi.org/10.48550/arXiv.2211.02263","url":null,"abstract":"Machine learning is the study of computer algorithms that can automatically improve based on data and experience. Machine learning algorithms build a model from sample data, called training data, to make predictions or judgments without being explicitly programmed to do so. A variety of wellknown machine learning algorithms have been developed for use in the field of computer science to analyze data. This paper introduced a new machine learning algorithm called impact learning. Impact learning is a supervised learning algorithm that can be consolidated in both classification and regression problems. It can furthermore manifest its superiority in analyzing competitive data. This algorithm is remarkable for learning from the competitive situation and the competition comes from the effects of autonomous features. It is prepared by the impacts of the highlights from the intrinsic rate of natural increase (RNI). We, moreover, manifest the prevalence of the impact learning over the conventional machine learning algorithm.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"48 1","pages":"102011"},"PeriodicalIF":0.0,"publicationDate":"2022-11-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86066518","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: 2
Stable and efficient time second-order difference schemes for fractional Klein-Gordon-Zakharov system 分数阶Klein-Gordon-Zakharov系统稳定有效的时间二阶差分格式
J. Comput. Sci. Pub Date : 2022-11-01 DOI: 10.1016/j.jocs.2022.101901
Jianqiang Xie, Quanxiang Wang, Zhiyue Zhang
{"title":"Stable and efficient time second-order difference schemes for fractional Klein-Gordon-Zakharov system","authors":"Jianqiang Xie, Quanxiang Wang, Zhiyue Zhang","doi":"10.1016/j.jocs.2022.101901","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101901","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"190 1","pages":"101901"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77697587","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
TransFlowNet: A physics-constrained Transformer framework for spatio-temporal super-resolution of flow simulations TransFlowNet:用于流体模拟时空超分辨率的物理约束Transformer框架
J. Comput. Sci. Pub Date : 2022-11-01 DOI: 10.1016/j.jocs.2022.101906
Xinjie Wang, Siyuan Zhu, Yundong Guo, Peng Han, Yucheng Wang, Zhiqiang Wei, Xiaogang Jin
{"title":"TransFlowNet: A physics-constrained Transformer framework for spatio-temporal super-resolution of flow simulations","authors":"Xinjie Wang, Siyuan Zhu, Yundong Guo, Peng Han, Yucheng Wang, Zhiqiang Wei, Xiaogang Jin","doi":"10.1016/j.jocs.2022.101906","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101906","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"1 1","pages":"101906"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90800013","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: 5
A parallel algorithm for maximal cliques enumeration to improve hypergraph construction 一种改进超图构造的最大团枚举并行算法
J. Comput. Sci. Pub Date : 2022-11-01 DOI: 10.1016/j.jocs.2022.101905
Xiang Gao, Fan Zhou, Kedi Xu, Xiang Tian, Yao-wu Chen
{"title":"A parallel algorithm for maximal cliques enumeration to improve hypergraph construction","authors":"Xiang Gao, Fan Zhou, Kedi Xu, Xiang Tian, Yao-wu Chen","doi":"10.1016/j.jocs.2022.101905","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101905","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"54 1","pages":"101905"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77072557","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: 1
Ensemble data assimilation using optimal control in the Wasserstein metric 在Wasserstein度量中使用最优控制的集成数据同化
J. Comput. Sci. Pub Date : 2022-11-01 DOI: 10.1016/j.jocs.2022.101895
Xin Liu, J. Frank
{"title":"Ensemble data assimilation using optimal control in the Wasserstein metric","authors":"Xin Liu, J. Frank","doi":"10.1016/j.jocs.2022.101895","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101895","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"19 1","pages":"101895"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76488384","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
Enhanced expected hypervolume improvement criterion for parallel multi-objective optimization 改进的并行多目标优化期望超容量改进准则
J. Comput. Sci. Pub Date : 2022-11-01 DOI: 10.1016/j.jocs.2022.101903
Qingyu Wang, Takuji Nakashima, Chenguang Lai, Bo Hu, Xinru Du, Zhongzheng Fu, Taiga Kanehira, Y. Konishi, Hiroyuki Okuizumi, Hidemi Mutsuda
{"title":"Enhanced expected hypervolume improvement criterion for parallel multi-objective optimization","authors":"Qingyu Wang, Takuji Nakashima, Chenguang Lai, Bo Hu, Xinru Du, Zhongzheng Fu, Taiga Kanehira, Y. Konishi, Hiroyuki Okuizumi, Hidemi Mutsuda","doi":"10.1016/j.jocs.2022.101903","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101903","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"104 1","pages":"101903"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75988432","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
Soft isogeometric analysis of the Bound States of a Quantum Three-Body Problem in 1D 一维量子三体问题束缚态的软等几何分析
J. Comput. Sci. Pub Date : 2022-10-13 DOI: 10.48550/arXiv.2210.06832
Danyang Li, Quanling Deng
{"title":"Soft isogeometric analysis of the Bound States of a Quantum Three-Body Problem in 1D","authors":"Danyang Li, Quanling Deng","doi":"10.48550/arXiv.2210.06832","DOIUrl":"https://doi.org/10.48550/arXiv.2210.06832","url":null,"abstract":"The study of quantum three-body problems has been centered on low-energy states that rely on accurate numerical approximation. Recently, isogeometric analysis (IGA) has been adopted to solve the problem as an alternative but more robust (with respect to atom mass ratios) method that outperforms the classical Born-Oppenheimer (BO) approximation. In this paper, we focus on the performance of IGA and apply the recently-developed softIGA to reduce the spectral errors of the low-energy bound states. The main idea is to add high-order derivative-jump terms with a penalty parameter to the IGA bilinear forms. With an optimal choice of the penalty parameter, we observe eigenvalue error superconvergence. We focus on linear (finite elements) and quadratic elements and demonstrate the outperformance of softIGA over IGA through a variety of examples including both two- and three-body problems in 1D.","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"3 1","pages":"102032"},"PeriodicalIF":0.0,"publicationDate":"2022-10-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78255565","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: 1
The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts 领域驱动和数据驱动特征选择对纳米颗粒催化剂反设计的影响
J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101896
Sichao Li, Jonathan Y. C. Ting, A. Barnard
{"title":"The impact of domain-driven and data-driven feature selection on the inverse design of nanoparticle catalysts","authors":"Sichao Li, Jonathan Y. C. Ting, A. Barnard","doi":"10.1016/j.jocs.2022.101896","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101896","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"14 1","pages":"101896"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82504722","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: 2
A machine learning framework for identifying influenza pneumonia from bacterial pneumonia for medical decision making 用于识别流行性肺炎和细菌性肺炎的机器学习框架,用于医疗决策
J. Comput. Sci. Pub Date : 2022-10-01 DOI: 10.1016/j.jocs.2022.101871
Qian Zhang, Anran Huang, Lianyou Shao, Peiliang Wu, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen, F. Alotaibi, Majdi M. Mafarja, Jinsheng Ouyang
{"title":"A machine learning framework for identifying influenza pneumonia from bacterial pneumonia for medical decision making","authors":"Qian Zhang, Anran Huang, Lianyou Shao, Peiliang Wu, Ali Asghar Heidari, Zhennao Cai, Guoxi Liang, Huiling Chen, F. Alotaibi, Majdi M. Mafarja, Jinsheng Ouyang","doi":"10.1016/j.jocs.2022.101871","DOIUrl":"https://doi.org/10.1016/j.jocs.2022.101871","url":null,"abstract":"","PeriodicalId":14601,"journal":{"name":"J. Comput. Sci.","volume":"07 1","pages":"101871"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86019575","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: 3
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