Advanced Modeling and Simulation in Engineering Sciences最新文献_第7页

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-05-25 DOI: 10.1186/s40323-022-00219-7

Giampaolo, Fabio, De Rosa, Mariapia, Qi, Pian, Izzo, Stefano, Cuomo, Salvatore

{"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":"30 8","pages":""},"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

Advanced Modeling and Simulation in Engineering Sciences 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

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-05-16 DOI: 10.1186/s40323-022-00217-9

M. Chapelier, R. Bouclier, J. Passieux

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-03-11 DOI: 10.1186/s40323-022-00215-x

N. Akkari, F. Casenave, D. Ryckelynck, C. Rey

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-03-07 DOI: 10.1186/s40323-022-00216-w

M. Kazemzadeh-Parsi, A. Ammar, F. Chinesta

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-02-18 DOI: 10.1186/s40323-022-00214-y

Hanane Khatouri, T. Benamara, P. Breitkopf, Jean Demange

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-01-05 DOI: 10.21203/rs.3.rs-1153344/v1

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":"10.21203/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":"9 1","pages":"1-28"},"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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2021-12-22 DOI: 10.1186/s40323-023-00242-2

Saddam Hijazi, M. Freitag, Niels Landwehr

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2021-12-10 DOI: 10.1186/s40323-022-00231-x

M. Xiao, Jun Ma, Dongcheng Lu, B. Raghavan, Weihong Zhang

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

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2021-12-01 DOI: 10.1186/s40323-021-00212-6

E. S. Shoukralla, Nermin Saber, A. Y. Sayed

引用次数: 2