首页 > 最新文献

Advanced Modeling and Simulation in Engineering Sciences最新文献

英文 中文
Physics-informed neural networks approach for 1D and 2D Gray-Scott systems 一维和二维Gray-Scott系统的物理信息神经网络方法
Q1 Mathematics Pub Date : 2022-05-25 DOI: 10.1186/s40323-022-00219-7
Giampaolo, Fabio, De Rosa, Mariapia, Qi, Pian, Izzo, Stefano, Cuomo, Salvatore
Nowadays, in the Scientific Machine Learning (SML) research field, the traditional machine learning (ML) tools and scientific computing approaches are fruitfully intersected for solving problems modelled by Partial Differential Equations (PDEs) in science and engineering applications. Challenging SML methodologies are the new computational paradigms named Physics-Informed Neural Networks (PINNs). PINN has revolutionized the classical adoption of ML in scientific computing, representing a novel class of promising algorithms where the learning process is constrained to satisfy known physical laws described by differential equations. In this paper, we propose a PINN-based computational study to deal with a non-linear partial differential equations system. In particular, using this approach, we solve the Gray-Scott model, a reaction–diffusion system that involves an irreversible chemical reaction between two reactants. In the unstable region of the model, we consider some a priori information related to dynamical behaviors, i. e. a supervised approach that relies on a finite difference method (FDM). Finally, simulation results show that PINNs can successfully provide an approximated Grey-Scott system solution, reproducing the characteristic Turing patterns for different parameter configurations.
目前,在科学机器学习(SML)研究领域,传统的机器学习(ML)工具和科学计算方法在解决科学和工程应用中的偏微分方程(PDEs)建模问题方面取得了丰硕的成果。具有挑战性的SML方法是新的计算范式,称为物理信息神经网络(pinn)。PINN彻底改变了ML在科学计算中的经典应用,代表了一类新的有前途的算法,其中学习过程受到约束,以满足由微分方程描述的已知物理定律。本文提出了一种基于pup的非线性偏微分方程组的计算方法。特别是,使用这种方法,我们解决了Gray-Scott模型,这是一个涉及两个反应物之间不可逆化学反应的反应扩散系统。在模型的不稳定区域,我们考虑了一些与动力学行为相关的先验信息,即依赖于有限差分法(FDM)的监督方法。最后,仿真结果表明,pinn可以成功地提供近似的gray - scott系统解,再现不同参数配置下的特征图灵模式。
{"title":"Physics-informed neural networks approach for 1D and 2D Gray-Scott systems","authors":"Giampaolo, Fabio, De Rosa, Mariapia, Qi, Pian, Izzo, Stefano, Cuomo, Salvatore","doi":"10.1186/s40323-022-00219-7","DOIUrl":"https://doi.org/10.1186/s40323-022-00219-7","url":null,"abstract":"Nowadays, in the Scientific Machine Learning (SML) research field, the traditional machine learning (ML) tools and scientific computing approaches are fruitfully intersected for solving problems modelled by Partial Differential Equations (PDEs) in science and engineering applications. Challenging SML methodologies are the new computational paradigms named Physics-Informed Neural Networks (PINNs). PINN has revolutionized the classical adoption of ML in scientific computing, representing a novel class of promising algorithms where the learning process is constrained to satisfy known physical laws described by differential equations. In this paper, we propose a PINN-based computational study to deal with a non-linear partial differential equations system. In particular, using this approach, we solve the Gray-Scott model, a reaction–diffusion system that involves an irreversible chemical reaction between two reactants. In the unstable region of the model, we consider some a priori information related to dynamical behaviors, i. e. a supervised approach that relies on a finite difference method (FDM). Finally, simulation results show that PINNs can successfully provide an approximated Grey-Scott system solution, reproducing the characteristic Turing patterns for different parameter configurations.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138510035","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: 9
Finite element method-enhanced neural network for forward and inverse problems 正反问题的有限元增强神经网络
Q1 Mathematics Pub Date : 2022-05-17 DOI: 10.1186/s40323-023-00243-1
R. Meethal, A. Kodakkal, Mohamed Khalil, A. Ghantasala, B. Obst, K. Bletzinger, R. Wüchner
{"title":"Finite element method-enhanced neural network for forward and inverse problems","authors":"R. Meethal, A. Kodakkal, Mohamed Khalil, A. Ghantasala, B. Obst, K. Bletzinger, R. Wüchner","doi":"10.1186/s40323-023-00243-1","DOIUrl":"https://doi.org/10.1186/s40323-023-00243-1","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41301471","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: 4
Spline-based specimen shape optimization for robust material model calibration 基于样条曲线的稳健材料模型标定试件形状优化
Q1 Mathematics Pub Date : 2022-05-16 DOI: 10.1186/s40323-022-00217-9
M. Chapelier, R. Bouclier, J. Passieux
{"title":"Spline-based specimen shape optimization for robust material model calibration","authors":"M. Chapelier, R. Bouclier, J. Passieux","doi":"10.1186/s40323-022-00217-9","DOIUrl":"https://doi.org/10.1186/s40323-022-00217-9","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-05-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47862139","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 updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems 一个更新的Gappy-POD捕捉流体动力学问题的非参数化几何变化
Q1 Mathematics Pub Date : 2022-03-11 DOI: 10.1186/s40323-022-00215-x
N. Akkari, F. Casenave, D. Ryckelynck, C. Rey
{"title":"An updated Gappy-POD to capture non-parameterized geometrical variation in fluid dynamics problems","authors":"N. Akkari, F. Casenave, D. Ryckelynck, C. Rey","doi":"10.1186/s40323-022-00215-x","DOIUrl":"https://doi.org/10.1186/s40323-022-00215-x","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44599949","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
Domain decomposition involving subdomain separable space representations for solving parametric problems in complex geometries 涉及子域可分离空间表示的复杂几何参数问题的域分解
Q1 Mathematics Pub Date : 2022-03-07 DOI: 10.1186/s40323-022-00216-w
M. Kazemzadeh-Parsi, A. Ammar, F. Chinesta
{"title":"Domain decomposition involving subdomain separable space representations for solving parametric problems in complex geometries","authors":"M. Kazemzadeh-Parsi, A. Ammar, F. Chinesta","doi":"10.1186/s40323-022-00216-w","DOIUrl":"https://doi.org/10.1186/s40323-022-00216-w","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"44537571","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
Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey 基于cpu密集仿真的设计优化的元建模技术:综述
Q1 Mathematics Pub Date : 2022-02-18 DOI: 10.1186/s40323-022-00214-y
Hanane Khatouri, T. Benamara, P. Breitkopf, Jean Demange
{"title":"Metamodeling techniques for CPU-intensive simulation-based design optimization: a survey","authors":"Hanane Khatouri, T. Benamara, P. Breitkopf, Jean Demange","doi":"10.1186/s40323-022-00214-y","DOIUrl":"https://doi.org/10.1186/s40323-022-00214-y","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"65854177","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
Spline-based specimen shape optimization for robust material model calibration 基于样条的试件形状优化鲁棒材料模型校准
Q1 Mathematics Pub Date : 2022-01-05 DOI: 10.21203/rs.3.rs-1153344/v1
M. Chapelier, R. Bouclier, J. Passieux
Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors use empirically defined geometries (with holes, notches...). The first attempts to optimize the specimen to maximize the sensitivity of the measurement are linked to a design space that is either very small (parametric optimization), which does not allow the exploration of very different designs, or, conversely, very large (topology optimization), which sometimes leads to designs that are not regular and cannot be manufactured. In this paper, an intermediate approach based on a non-invasive CAD-inspired optimization strategy is proposed. It relies on the definition of univariate spline Free-Form Deformation boxes to reduce the design space and thus regularize the problem. Then, from the modeling point of view, a new objective function is proposed that takes into account the experimental setup and constraint functions are added to ensure that the gain is real and the shape physically sound. Several examples show that with this method and at low cost, one can significantly improve the identification of constitutive parameters without changing the experimental setup.
现场测量识别允许从单个测试中识别多个参数,前提是测量对要识别的参数足够敏感。为了做到这一点,作者使用经验定义的几何形状(有孔、缺口……)。优化样品以最大化测量灵敏度的第一次尝试与设计空间相关联,该设计空间要么很小(参数优化),这不允许探索非常不同的设计,或者相反,非常大(拓扑优化),这有时会导致不规则的设计,无法制造。本文提出了一种基于非侵入式cad优化策略的中间方法。它依赖于单变量样条自由变形盒的定义来减小设计空间,从而使问题正则化。然后,从建模的角度出发,提出了一个考虑实验设置的新目标函数,并增加了约束函数,以确保增益真实,形状物理合理。实例表明,该方法在不改变实验装置的情况下,以较低的成本显著提高了本构参数的识别效果。
{"title":"Spline-based specimen shape optimization for robust material model calibration","authors":"M. Chapelier, R. Bouclier, J. Passieux","doi":"10.21203/rs.3.rs-1153344/v1","DOIUrl":"https://doi.org/10.21203/rs.3.rs-1153344/v1","url":null,"abstract":"Identification from field measurements allows several parameters to be identified from a single test, provided that the measurements are sensitive enough to the parameters to be identified. To do this, authors use empirically defined geometries (with holes, notches...). The first attempts to optimize the specimen to maximize the sensitivity of the measurement are linked to a design space that is either very small (parametric optimization), which does not allow the exploration of very different designs, or, conversely, very large (topology optimization), which sometimes leads to designs that are not regular and cannot be manufactured. In this paper, an intermediate approach based on a non-invasive CAD-inspired optimization strategy is proposed. It relies on the definition of univariate spline Free-Form Deformation boxes to reduce the design space and thus regularize the problem. Then, from the modeling point of view, a new objective function is proposed that takes into account the experimental setup and constraint functions are added to ensure that the gain is real and the shape physically sound. Several examples show that with this method and at low cost, one can significantly improve the identification of constitutive parameters without changing the experimental setup.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48655754","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
POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations 求解Navier-Stokes方程反问题的POD Galerkin降阶模型和基于物理的神经网络
Q1 Mathematics Pub Date : 2021-12-22 DOI: 10.1186/s40323-023-00242-2
Saddam Hijazi, M. Freitag, Niels Landwehr
{"title":"POD-Galerkin reduced order models and physics-informed neural networks for solving inverse problems for the Navier–Stokes equations","authors":"Saddam Hijazi, M. Freitag, Niels Landwehr","doi":"10.1186/s40323-023-00242-2","DOIUrl":"https://doi.org/10.1186/s40323-023-00242-2","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42836277","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: 6
Stress-constrained topology optimization using approximate reanalysis with on-the-fly reduced order modeling 基于近似再分析和动态降阶建模的应力约束拓扑优化
Q1 Mathematics Pub Date : 2021-12-10 DOI: 10.1186/s40323-022-00231-x
M. Xiao, Jun Ma, Dongcheng Lu, B. Raghavan, Weihong Zhang
{"title":"Stress-constrained topology optimization using approximate reanalysis with on-the-fly reduced order modeling","authors":"M. Xiao, Jun Ma, Dongcheng Lu, B. Raghavan, Weihong Zhang","doi":"10.1186/s40323-022-00231-x","DOIUrl":"https://doi.org/10.1186/s40323-022-00231-x","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43501253","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
Computational method for solving weakly singular Fredholm integral equations of the second kind using an advanced barycentric Lagrange interpolation formula 利用先进的重心拉格朗日插值公式求解第二类弱奇异Fredholm积分方程的计算方法
Q1 Mathematics Pub Date : 2021-12-01 DOI: 10.1186/s40323-021-00212-6
E. S. Shoukralla, Nermin Saber, A. Y. Sayed
{"title":"Computational method for solving weakly singular Fredholm integral equations of the second kind using an advanced barycentric Lagrange interpolation formula","authors":"E. S. Shoukralla, Nermin Saber, A. Y. Sayed","doi":"10.1186/s40323-021-00212-6","DOIUrl":"https://doi.org/10.1186/s40323-021-00212-6","url":null,"abstract":"","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2021-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46448070","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
期刊
Advanced Modeling and Simulation in Engineering Sciences
全部 Acc. Chem. Res. ACS Applied Bio Materials ACS Appl. Electron. Mater. ACS Appl. Energy Mater. ACS Appl. Mater. Interfaces ACS Appl. Nano Mater. ACS Appl. Polym. Mater. ACS BIOMATER-SCI ENG ACS Catal. ACS Cent. Sci. ACS Chem. Biol. ACS Chemical Health & Safety ACS Chem. Neurosci. ACS Comb. Sci. ACS Earth Space Chem. ACS Energy Lett. ACS Infect. Dis. ACS Macro Lett. ACS Mater. Lett. ACS Med. Chem. Lett. ACS Nano ACS Omega ACS Photonics ACS Sens. ACS Sustainable Chem. Eng. ACS Synth. Biol. Anal. Chem. BIOCHEMISTRY-US Bioconjugate Chem. BIOMACROMOLECULES Chem. Res. Toxicol. Chem. Rev. Chem. Mater. CRYST GROWTH DES ENERG FUEL Environ. Sci. Technol. Environ. Sci. Technol. Lett. Eur. J. Inorg. Chem. IND ENG CHEM RES Inorg. Chem. J. Agric. Food. Chem. J. Chem. Eng. Data J. Chem. Educ. J. Chem. Inf. Model. J. Chem. Theory Comput. J. Med. Chem. J. Nat. Prod. J PROTEOME RES J. Am. Chem. Soc. LANGMUIR MACROMOLECULES Mol. Pharmaceutics Nano Lett. Org. Lett. ORG PROCESS RES DEV ORGANOMETALLICS J. Org. Chem. J. Phys. Chem. J. Phys. Chem. A J. Phys. Chem. B J. Phys. Chem. C J. Phys. Chem. Lett. Analyst Anal. Methods Biomater. Sci. Catal. Sci. Technol. Chem. Commun. Chem. Soc. Rev. CHEM EDUC RES PRACT CRYSTENGCOMM Dalton Trans. Energy Environ. Sci. ENVIRON SCI-NANO ENVIRON SCI-PROC IMP ENVIRON SCI-WAT RES Faraday Discuss. Food Funct. Green Chem. Inorg. Chem. Front. Integr. Biol. J. Anal. At. Spectrom. J. Mater. Chem. A J. Mater. Chem. B J. Mater. Chem. C Lab Chip Mater. Chem. Front. Mater. Horiz. MEDCHEMCOMM Metallomics Mol. Biosyst. Mol. Syst. Des. Eng. Nanoscale Nanoscale Horiz. Nat. Prod. Rep. New J. Chem. Org. Biomol. Chem. Org. Chem. Front. PHOTOCH PHOTOBIO SCI PCCP Polym. Chem.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1