Advanced Modeling and Simulation in Engineering Sciences最新文献_第4页

Regularized regressions for parametric models based on separated representations 基于分离表示的参数模型正则化回归

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-03-09 DOI: 10.1186/s40323-023-00240-4

Abel Sancarlos, V. Champaney, E. Cueto, F. Chinesta

引用次数: 2

A DeepONet multi-fidelity approach for residual learning in reduced order modeling 一种用于降阶建模残差学习的DeepONet高保真度方法

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-02-24 DOI: 10.1186/s40323-023-00249-9

N. Demo, M. Tezzele, G. Rozza

引用次数: 4

Damage model for simulating cohesive fracture behavior of multi-phase composite materials 模拟多相复合材料黏聚断裂行为的损伤模型

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-02-06 DOI: 10.1186/s40323-022-00238-4

M. Kurumatani, Takumi Kato, Hiromu Sasaki

引用次数: 0

Seepage failure prediction of breakwater using an unresolved ISPH-DEM coupling method enriched with Terzaghi’s critical hydraulic gradient 用富含Terzaghi临界水力梯度的未解决ISPH-DEM耦合方法预测防波堤的渗透破坏

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-23 DOI: 10.1186/s40323-022-00239-3

Kumpei Tsuji, M. Asai, K. Kasama

引用次数: 1

Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders. 利用深度空间和时间卷积自动编码器为随机大规模和时间依赖性流动问题进行降阶建模。

IF 2

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 Epub Date: 2023-05-19 DOI: 10.1186/s40323-023-00244-0

Azzedine Abdedou, Azzeddine Soulaimani

{"title":"Reduced-order modeling for stochastic large-scale and time-dependent flow problems using deep spatial and temporal convolutional autoencoders.","authors":"Azzedine Abdedou, Azzeddine Soulaimani","doi":"10.1186/s40323-023-00244-0","DOIUrl":"10.1186/s40323-023-00244-0","url":null,"abstract":"A non-intrusive reduced-order model based on convolutional autoencoders is proposed as a data-driven tool to build an efficient nonlinear reduced-order model for stochastic spatiotemporal large-scale flow problems. The objective is to perform accurate and rapid uncertainty analyses of the flow outputs of interest for which the input parameters are deemed uncertain. The data are constituted from a set of high-fidelity snapshots, collected using an inhouse high-fidelity flow solver, which correspond to a sample of the uncertain input parameters. The method uses a 1D-convolutional autoencoder to reduce the spatial dimension of the unstructured meshes used by the flow solver. Another convolutional autoencoder is used for the time compression. The encoded latent vectors, generated from the two compression levels, are then mapped to the input parameters using a regression-based multilayer perceptron. The proposed model allows for rapid predictions for unseen parameter values, allowing the output statistical moments to be computed efficiently. The accuracy of the proposed approach is compared to that of the linear reduced-order technique based on an artificial neural network through two benchmark tests (the one-dimensional Burgers and Stoker's solutions) and a hypothetical dam break flow problem, with an unstructured mesh and over a complex bathymetry river. The numerical results show that the proposed methods present strong predictive capabilities to accurately approximate the statistical moments of the outputs. In particular, the predicted statistical moments are oscillations-free, unlike those obtained with the traditional proper orthogonal decomposition method. The proposed reduction framework is simple to implement and can be applied to other parametric and time-dependent problems governed by partial differential equations, which are commonly encountered in many engineering and science problems.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"7"},"PeriodicalIF":2.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10198944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9506104","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A "data-driven uncertainty" computational method to model and predict instabilities of a frictional system. 一种“数据驱动的不确定性”计算方法来模拟和预测摩擦系统的不稳定性。

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 DOI: 10.1186/s40323-023-00241-3

Farouk Maaboudallah, Noureddine Atalla

{"title":"A \"data-driven uncertainty\" computational method to model and predict instabilities of a frictional system.","authors":"Farouk Maaboudallah, Noureddine Atalla","doi":"10.1186/s40323-023-00241-3","DOIUrl":"https://doi.org/10.1186/s40323-023-00241-3","url":null,"abstract":"Most of the recently developed methods for predicting instabilities of frictional systems couple stochastic algorithms with the finite element method (FEM). They use random variables to model the uncertainty of input parameters through standard probability laws. Regardless of the fact that advanced numerical schemes are available nowadays, a systematic and accurate method to describe finely the uncertainties upstream the model, and thus predict its response is still missing. In this contribution, we present a data-driven stochastic finite element scheme to predict the dynamic behavior of a rubbing system. The proposed framework relies on data-driven approach and uses four steps. In the first, the measured data are integrated directly, for the uncertainty quantification, by means of the random balance design (RBD). In the second step, the generated stochastic data are evaluated in an iterative way to solve friction-induced vibration problem. In the third step, the resulted data are reordered in such a way that the corresponding values of each measured input parameters are ranked in ascending order. Finally, the Fourier spectrum is introduced on the reordered results to compute the sensitivity indices. Thus, instead of Monte Carlo-based formalism or Fourier Amplitude Sensitivity Test (FAST), the computational cost of the proposed method is kept down to <math><mrow><mi>O</mi> <mo>(</mo> <mi>N</mi> <mo>)</mo></mrow> </math> with N the number of samples. We investigate the efficiency of the suggested solver on a reduced brake system. Altogether, the suggested procedure achieves excellent accuracy at a much reduced computational time compared to the methods available in the literature.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"3"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9925556/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10824088","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems. 时变流问题中外推预测的深度卷积架构。

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2023-01-01 Epub Date: 2023-11-30 DOI: 10.1186/s40323-023-00254-y

Pratyush Bhatt, Yash Kumar, Azzeddine Soulaïmani

{"title":"Deep convolutional architectures for extrapolative forecasts in time-dependent flow problems.","authors":"Pratyush Bhatt, Yash Kumar, Azzeddine Soulaïmani","doi":"10.1186/s40323-023-00254-y","DOIUrl":"10.1186/s40323-023-00254-y","url":null,"abstract":"Physical systems whose dynamics are governed by partial differential equations (PDEs) find numerous applications in science and engineering. The process of obtaining the solution from such PDEs may be computationally expensive for large-scale and parameterized problems. In this work, deep learning techniques developed especially for time-series forecasts, such as LSTM and TCN, or for spatial-feature extraction such as CNN, are employed to model the system dynamics for advection-dominated problems. This paper proposes a Convolutional Autoencoder(CAE) model for compression and a CNN future-step predictor for forecasting. These models take as input a sequence of high-fidelity vector solutions for consecutive time steps obtained from the PDEs and forecast the solutions for the subsequent time steps using auto-regression; thereby reducing the computation time and power needed to obtain such high-fidelity solutions. Non-intrusive reduced-order modeling techniques such as deep auto-encoder networks are utilized to compress the high-fidelity snapshots before feeding them as input to the forecasting models in order to reduce the complexity and the required computations in the online and offline stages. The models are tested on numerical benchmarks (1D Burgers' equation and Stoker's dam-break problem) to assess the long-term prediction accuracy, even outside the training domain (i.e. extrapolation). The most accurate model is then used to model a hypothetical dam break in a river with complex 2D bathymetry. The proposed CNN future-step predictor revealed much more accurate forecasting than LSTM and TCN in the considered spatiotemporal problems.","PeriodicalId":37424,"journal":{"name":"Advanced Modeling and Simulation in Engineering Sciences","volume":"10 1","pages":"17"},"PeriodicalIF":0.0,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10689563/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"138478794","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

The Pseudo-Direct Numerical Simulation Method considered as a Reduced Order Model 作为降阶模型的伪直接数值模拟方法

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-10-28 DOI: 10.1186/s40323-022-00235-7

S. Idelsohn, J. M. Giménez, N. Nigro

引用次数: 1

Empowering engineering with data, machine learning and artificial intelligence: a short introductive review 用数据、机器学习和人工智能赋能工程：一篇简短的介绍性综述

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-10-27 DOI: 10.1186/s40323-022-00234-8

F. Chinesta, E. Cueto

引用次数: 8

A deformation-dependent coupled Lagrangian/semi-Lagrangian meshfree hydromechanical formulation for landslide modeling 滑坡模型的变形相关耦合拉格朗日/半拉格朗日无网格流体力学公式

Advanced Modeling and Simulation in Engineering Sciences Pub Date : 2022-09-30 DOI: 10.1186/s40323-022-00233-9

Jonghyuk Baek, Ryan T. Schlinkman, Frank N. Beckwith, Jiun-Shyan Chen

引用次数: 3