Computation最新文献_第10页

Adapting PINN Models of Physical Entities to Dynamical Data 物理实体的PINN模型对动态数据的适应性

IF 2.2

Computation Pub Date : 2023-09-01 DOI: 10.3390/computation11090168

Dmitriy Tarkhov, T. Lazovskaya, V. Antonov

{"title":"Adapting PINN Models of Physical Entities to Dynamical Data","authors":"Dmitriy Tarkhov, T. Lazovskaya, V. Antonov","doi":"10.3390/computation11090168","DOIUrl":"https://doi.org/10.3390/computation11090168","url":null,"abstract":"This article examines the possibilities of adapting approximate solutions of boundary value problems for differential equations using physics-informed neural networks (PINNs) to changes in data about the physical entity being modelled. Two types of models are considered: PINN and parametric PINN (PPINN). The former is constructed for a fixed parameter of the problem, while the latter includes the parameter for the number of input variables. The models are tested on three problems. The first problem involves modelling the bending of a cantilever rod under varying loads. The second task is a non-stationary problem of a thermal explosion in the plane-parallel case. The initial model is constructed based on an ordinary differential equation, while the modelling object satisfies a partial differential equation. The third task is to solve a partial differential equation of mixed type depending on time. In all cases, the initial models are adapted to the corresponding pseudo-measurements generated based on changing equations. A series of experiments are carried out for each problem with different functions of a parameter that reflects the character of changes in the object. A comparative analysis of the quality of the PINN and PPINN models and their resistance to data changes has been conducted for the first time in this study.","PeriodicalId":52148,"journal":{"name":"Computation","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47755598","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

Shear-Enhanced Compaction Analysis of the Vaca Muerta Formation 瓦卡穆埃尔塔地层的剪切增强压实分析

IF 2.2

Computation Pub Date : 2023-08-29 DOI: 10.3390/computation11120250

J. Hasbani, E. Kias, R. Suarez-Rivera, V. Calo

引用次数: 0

Numerical Computation of Hydrodynamic Characteristics of an Automated Hand-Washing System 自动洗手系统水动力特性的数值计算

IF 2.2

Computation Pub Date : 2023-08-22 DOI: 10.3390/computation11090167

Thanh-Long Le, Thi-Hong-Nhi Vuong, Trandinh Phung

引用次数: 1

Evolutionary PINN Learning Algorithms Inspired by Approximation to Pareto Front for Solving Ill-Posed Problems Pareto前沿逼近启发的进化PINN学习算法求解病态问题

IF 2.2

Computation Pub Date : 2023-08-21 DOI: 10.3390/computation11080166

T. Lazovskaya, D. Tarkhov, Maria Chistyakova, Egor Razumov, Anna Sergeeva, T. Shemyakina

{"title":"Evolutionary PINN Learning Algorithms Inspired by Approximation to Pareto Front for Solving Ill-Posed Problems","authors":"T. Lazovskaya, D. Tarkhov, Maria Chistyakova, Egor Razumov, Anna Sergeeva, T. Shemyakina","doi":"10.3390/computation11080166","DOIUrl":"https://doi.org/10.3390/computation11080166","url":null,"abstract":"The article presents the development of new physics-informed evolutionary neural network learning algorithms. These algorithms aim to address the challenges of ill-posed problems by constructing a population close to the Pareto front. The study focuses on comparing the algorithm’s capabilities based on three quality criteria of solutions. To evaluate the algorithms’ performance, two benchmark problems have been used. The first involved solving the Laplace equation in square regions with discontinuous boundary conditions. The second problem considered the absence of boundary conditions but with the presence of measurements. Additionally, the study investigates the influence of hyperparameters on the final results. Comparisons have been made between the proposed algorithms and standard algorithms for constructing neural networks based on physics (commonly referred to as vanilla’s algorithms). The results demonstrate the advantage of the proposed algorithms in achieving better performance when solving incorrectly posed problems. Furthermore, the proposed algorithms have the ability to identify specific solutions with the desired smoothness.","PeriodicalId":52148,"journal":{"name":"Computation","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42192924","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

Investigation of the Failure Response of Masonry Walls Subjected to Blast Loading Using Nonlinear Finite Element Analysis 爆破荷载作用下砌体墙体破坏响应的非线性有限元分析

IF 2.2

Computation Pub Date : 2023-08-21 DOI: 10.3390/computation11080165

Sipho G. Thango, G. Stavroulakis, G. Drosopoulos

{"title":"Investigation of the Failure Response of Masonry Walls Subjected to Blast Loading Using Nonlinear Finite Element Analysis","authors":"Sipho G. Thango, G. Stavroulakis, G. Drosopoulos","doi":"10.3390/computation11080165","DOIUrl":"https://doi.org/10.3390/computation11080165","url":null,"abstract":"A numerical investigation of masonry walls subjected to blast loads is presented in this article. A non-linear finite element model is proposed to describe the structural response of the walls. A unilateral contact–friction law is used in the interfaces of the masonry blocks to provide the discrete failure between the blocks. A continuum damage plasticity model is also used to account for the compressive and tensile failure of the blocks. The main goal of this article is to investigate the different collapse mechanisms that arise as an effect of the blast load parameters and the static load of the wall. Parametric studies are conducted to evaluate the effect of the blast source–wall (standoff) distance and the blast weight on the structural response of the system. It is shown that the traditional in-plane diagonal cracking failure mode may still dominate when a blast action is present, depending on the considered standoff distance and the blast weight when in-plane static loading is also applied to the wall. It is also highlighted that the presence of an opening in the wall may significantly reduce the effect of the blasting action.","PeriodicalId":52148,"journal":{"name":"Computation","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43468922","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

Diffusion Kinetics Theory of Removal of Assemblies’ Surface Deposits with Flushing Oil 冲洗油去除组件表面沉积物的扩散动力学理论

IF 2.2

Computation Pub Date : 2023-08-20 DOI: 10.3390/computation11080164

M. Vigdorowitsch, V. Ostrikov, Alexander N. Pchelintsev, I. Pchelintseva

引用次数: 0

Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations 使用模型参考自适应控制、基于神经网络的参数不确定性补偿器和不同对象参数化的四旋翼轨迹跟踪

IF 2.2

Computation Pub Date : 2023-08-18 DOI: 10.3390/computation11080163

A. Glushchenko, K. Lastochkin

{"title":"Quadrotor Trajectory Tracking Using Model Reference Adaptive Control, Neural Network-Based Parameter Uncertainty Compensator, and Different Plant Parameterizations","authors":"A. Glushchenko, K. Lastochkin","doi":"10.3390/computation11080163","DOIUrl":"https://doi.org/10.3390/computation11080163","url":null,"abstract":"A quadrotor trajectory tracking problem is addressed via the design of a model reference adaptive control (MRAC) system. As for real-world applications, the entire quadrotor dynamics is typically unknown. To take that into account, we consider a plant model, which contains uncertain nonlinear terms resulting from aerodynamic friction, blade flapping, and the fact that the mass and inertia moments of the quadrotor may change from their nominal values. Unlike many known studies, the explicit equations of the parameter uncertainty for the position control loop are derived in two different ways using the differential flatness approach: the control signals are (i) used and (ii) not used in the parametric uncertainty parameterization. After analysis, the neural network (NN) is chosen for both cases as a compensator of such uncertainty, and the set of NN input signals is justified for each of them. Unlike many known MRAC systems with NN for quadrotors, in this study, we use the kxx+krr baseline controller, which follows from the control system derivation, with both time-invariant (parameterization (i)) and adjustable (parameterization (ii)) parameters instead of an arbitrarily chosen non-tunable PI/PD/PID-like one. Adaptive laws are derived to adjust the parameters of NN uncertainty compensator for both parameterizations. As a result, the position controller ensures the asymptotic stability of the tracking error for both cases under the assumption of perfect attitude loop tracking, which is ensured in the system previously developed by the authors. The results of the numerical experiments support the theoretical conclusions and provide a comparison of the effectiveness of the derived parameterizations. They also allow us to make conclusions on the necessity of the baseline controller adjustment.","PeriodicalId":52148,"journal":{"name":"Computation","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46427879","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 Complexity of the Super Subdivision of Cycle-Related Graphs Using Block Matrices 循环相关图的块矩阵超细分的复杂性

IF 2.2

Computation Pub Date : 2023-08-15 DOI: 10.3390/computation11080162

Mohamed R. Zeen El Deen, Walaa A. Aboamer, H. El-Sherbiny

引用次数: 0

Sensitivity Analysis of Mathematical Models 数学模型的敏感性分析

IF 2.2

Computation Pub Date : 2023-08-14 DOI: 10.3390/computation11080159

A. Sysoev

{"title":"Sensitivity Analysis of Mathematical Models","authors":"A. Sysoev","doi":"10.3390/computation11080159","DOIUrl":"https://doi.org/10.3390/computation11080159","url":null,"abstract":"The construction of a mathematical model of a complicated system is often associated with the evaluation of inputs’ (arguments, factors) influence on the output (response), the identification of important relationships between the variables used, and reduction of the model by decreasing the number of its inputs. These tasks are related to the problems of Sensitivity Analysis of mathematical models. The author proposes an alternative approach based on applying Analysis of Finite Fluctuations that uses the Lagrange mean value theorem to estimate the contribution of changes to the variables of a function to the output change. The article investigates the presented approach on an example of a class of fully connected neural network models. As a result of Sensitivity Analysis, a set of sensitivity measures for each input is obtained. For their averaging, it is proposed to use a point-and-interval estimation algorithm using Tukey’s weighted average. The comparison of the described method with the computation of Sobol’s indices is given; the consistency of the proposed method is shown. The computational robustness of the procedure for finding sensitivity measures of inputs is investigated. Numerical experiments are carried out on the neuraldat data set of the NeuralNetTools library of the R data processing language and on data of the healthcare services provided in the Lipetsk region.","PeriodicalId":52148,"journal":{"name":"Computation","volume":" ","pages":""},"PeriodicalIF":2.2,"publicationDate":"2023-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47682031","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

Study on Optical Positioning Using Experimental Visible Light Communication System 基于实验可见光通信系统的光学定位研究

IF 2.2

Computation Pub Date : 2023-08-14 DOI: 10.3390/computation11080161

Nikoleta Vitsi, A. Stassinakis, N. A. Androutsos, G. D. Roumelas, G. K. Varotsos, K. Aidinis, H. Nistazakis

引用次数: 0