Journal of Machine Learning for Modeling and Computing最新文献_第2页

A neural ordinary differential equation framework for modeling inelastic stress response via internal state variables 基于内部状态变量的非弹性应力响应建模的神经常微分方程框架

Journal of Machine Learning for Modeling and Computing Pub Date : 2021-11-29 DOI: 10.1615/jmachlearnmodelcomput.2022042917

R. Jones, A. Frankel, K. Johnson

引用次数: 10

Leveraging Local Variation in Data: Sampling and Weighting Schemes for Supervised Deep Learning 利用数据的局部变化:监督深度学习的抽样和加权方案

Journal of Machine Learning for Modeling and Computing Pub Date : 2021-01-19 DOI: 10.1615/JMachLearnModelComput.2022041819

Paul Novello, G. Poette, D. Lugato, P. Congedo

引用次数: 0

Data-informed Emulators for Multi-Physics Simulations 多物理场仿真的数据通知模拟器

Journal of Machine Learning for Modeling and Computing Pub Date : 2020-12-31 DOI: 10.1615/jmachlearnmodelcomput.2021038577

Hannah Lu, D. Ermakova, H. Wainwright, Liange Zheng, D. Tartakovsky

引用次数: 7

Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations 学习控制方程和本构关系的多步和连续物理信息神经网络方法

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022041787

R. Tipireddy, P. Perdikaris, P. Stinis, A. Tartakovsky

{"title":"Multistep and continuous physics-informed neural network methods for learning governing equations and constitutive relations","authors":"R. Tipireddy, P. Perdikaris, P. Stinis, A. Tartakovsky","doi":"10.1615/jmachlearnmodelcomput.2022041787","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2022041787","url":null,"abstract":"We investigate the applicability and relative merit of discrete and continuous versions of physics-informed neural network (PINN) methods for learning unknown governing equations or constitutive relations in a nonlinear dynamical system. In the case of unknown dynamics, entire right-hand-side (RHS) equations of the ordinary differential equations are unknown. In the case of unknown constitutive relations, however, the RHS equations are known up to the speciﬁcation of constitutive relations (that may depend on the state of the system). We use a deep neural network to model unknown governing equations or constitutive relations. The discrete PINN approach combines clas-sical multistep discretization methods for dynamical systems with neural-network-based machine learning methods. On the other hand, the continuous versions utilize deep neural networks to minimize the residual function for the continuous governing equations. We use the case of a fedbatch bioreactor system to study the effectiveness of these approaches and discuss conditions for their applicability. Our results indicate that the accuracy of the trained neural network models is much higher for the cases where we only have to learn a constitutive relation instead of all dynamics. This ﬁnding corroborates the well-known fact from scientiﬁc computing that building as much structural information as is available into an algorithm can enhance its efﬁciency and/or accuracy.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"27 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131466905","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

Preface for Special Issue on Machine Learning Techniques for Applications in Sustainability Research 机器学习技术在可持续发展研究中的应用特刊前言

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2022045267

V. Ciriello, D. Tartakovsky

引用次数: 0

A scalable transformer model for real-time decision making in neutron scattering experiments 中子散射实验中实时决策的可伸缩变压器模型

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023048607

Junqi Yin, Siyan Liu, Viktor Reshniak, Xiaoping Wang, Guannan Zhang

引用次数: 0

Assessment of Neuroimaging Data and Identification of Alzheimer's Disease using Extreme Learning Machines 使用极限学习机评估阿尔茨海默病的神经影像学数据和识别

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023048413

D. Raghavan, Suma K V, Puneeth N Ganesh

{"title":"Assessment of Neuroimaging Data and Identification of Alzheimer's Disease using Extreme Learning Machines","authors":"D. Raghavan, Suma K V, Puneeth N Ganesh","doi":"10.1615/jmachlearnmodelcomput.2023048413","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2023048413","url":null,"abstract":"Alzheimer’s disease (AD), one of the most common forms of dementia, is a cognitive disorder that is progressive in nature and causes a dynamic deterioration of the mental state of an individual. It severely damages the brain cells, neurotransmitters, and nerves, leading to irreparable damage to the brain, which is one of the major causes of dementia. Early identiﬁcation, assessment, and timely diagnosis are of paramount importance to slow down the progression of the disease, which calls for the design and development of algorithms and technology-aided tools for accurate detection, diagnosis, and prediction of the severity of Alzheimer’s disease. To provide a solution to this, we propose an extreme learning machine (ELM) algorithm that is trained on neuroimaging data from longitudinal MRI scans obtained from the OASIS database. We adopt an extensive feature engineering pipeline to choose the most signiﬁcant features for early identiﬁcation of the onset of dementia. We obtain an overall accuracy of 98.3%, sensitivity of 0.956, speciﬁcity of 0.962, and F1 score of 0.972. We also show that our proposed ELM algorithm outperforms several other contemporary classiﬁers based on a range of evaluation metrics. The paper also provides a feasibility analysis of the proposed model for real-time clinical deployment.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"18 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128841592","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

Physics-Informed Neural Networks for Informed Vaccine Distribution in Meta-Populations 基于物理信息的神经网络在meta - population中的疫苗分布

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023047642

Alvan Arulandu, P. Seshaiyer

引用次数: 0

Letter from the Editors: The emerging need for biologically-inspired and mathematically-guided machine learning for knowledge discovery in biology 编辑来信:生物学启发和数学指导的机器学习对生物学知识发现的新需求

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2023049680

Ritambhara Singh, P. Seshaiyer

引用次数: 0

Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests 基于随机森林的次小时地下水时间序列连续间隙和极值反演

Journal of Machine Learning for Modeling and Computing Pub Date : 1900-01-01 DOI: 10.1615/jmachlearnmodelcomput.2021038774

D. Dwivedi, U. Mital, B. Faybishenko, B. Dafflon, C. Varadharajan, Deb Agarwal, K. Williams, C. Steefel, Susan Hubbard

{"title":"Imputation of Contiguous Gaps and Extremes of Subhourly Groundwater Time Series Using Random Forests","authors":"D. Dwivedi, U. Mital, B. Faybishenko, B. Dafflon, C. Varadharajan, Deb Agarwal, K. Williams, C. Steefel, Susan Hubbard","doi":"10.1615/jmachlearnmodelcomput.2021038774","DOIUrl":"https://doi.org/10.1615/jmachlearnmodelcomput.2021038774","url":null,"abstract":"Machine learning can provide sustainable solutions to gap-fill groundwater (GW) data needed to adequately constrain watershed models. However, imputing missing extremes is more challenging than other parts of a hydrograph. To impute missing subhourly data, including extremes, within GW time-series data collected at multiple wells in the East River watershed, located in southwestern Colorado, we consider a single-well imputation (SWI) and a multiple-well imputation (MWI) approach. SWI gap-fills missing GW entries in a well using the same well’s time-series data; MWI gap-fills a specific well’s missing GW entry using the time series of neighboring wells. SWI takes advantage of linear interpolation and random forest (RF) approaches, whereas MWI exploits only the RF approach. We also use an information entropy framework to develop insights into how missing data patterns impact imputation. We discovered that if gaps were at random intervals, SWI could accurately impute up to 90% of missing data over an approximately two-year period. Contiguous gaps constituted more complex scenarios for imputation and required the use of MWI. Information entropy suggested that if gaps were contiguous, up to 50% of missing GW data could be estimated accurately over an approximately two-year period. The RF-feature importance suggested that a time feature (months) and a space feature (neighboring wells) were the most important predictors in the SWI and MWI. We also noted that neither SWI nor MWI methods could capture the missing extremes of a hydrograph. To counter this, we developed a new sequential approach and demonstrated the imputation of missing extremes in a GW time series with high accuracy.","PeriodicalId":320604,"journal":{"name":"Journal of Machine Learning for Modeling and Computing","volume":"81 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133636231","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: 8