The research topics addressed by Advanced Modeling and Simulation in Engineering Sciences (AMSES) cover the vast domain of the advanced modeling and simulation of materials, processes and structures governed by the laws of mechanics. The emphasis is on advanced and innovative modeling approaches and numerical strategies. The main objective is to describe the actual physics of large mechanical systems with complicated geometries as accurately as possible using complex, highly nonlinear and coupled multiphysics and multiscale models, and then to carry out simulations with these complex models as rapidly as possible. In other words, this research revolves around efficient numerical modeling along with model verification and validation. Therefore, the corresponding papers deal with advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation and simulation-based control. These challenging issues require multidisciplinary efforts – particularly in modeling, numerical analysis and computer science – which are treated in this journal.

The journal "Advanced Modeling and Simulation in Engineering Sciences" addresses a wide range of topics related to the advanced modeling and simulation of materials, processes, and structures governed by the laws of mechanics. It emphasizes innovative modeling approaches and numerical strategies, aiming to accurately describe the actual physics of large mechanical systems with complex geometries. The core of this research revolves around efficient numerical modeling, along with model verification and validation. Consequently, the papers published in this journal cover advanced modeling and simulation, efficient optimization, inverse analysis, data-driven computation, and simulation-based control. These challenging issues require multidisciplinary collaboration, particularly in the fields of modeling, numerical analysis, and computer science, which are thoroughly explored in this journal.

《Advanced Modeling and Simulation in Engineering Sciences》期刊关注材料、过程和结构的先进建模与仿真，涉及力学规律下的广泛领域。期刊强调创新的建模方法和数值策略，旨在尽可能准确地描述复杂几何形状的大型机械系统的实际物理特性。研究的核心是高效的数值建模，以及模型的验证和确认。因此，相关论文涉及先进建模与仿真、高效优化、逆向分析、数据驱动计算和基于仿真的控制。这些具有挑战性的问题需要多学科的合作，特别是在建模、数值分析和计算机科学领域，这些内容在本期刊中得到了充分的探讨。

Advanced Modeling and Simulation in Engineering Sciences

Abstract Prior to any numerical development, the paper objective is to answer first to a fundamental question: what is the mathematical form of the most general data-driven constitutive model for stable materials, taking maximum account of knowledge from physics and materials science? Here we restrict ourselves to elasto-(visco-)plastic materials under the small displacement assumption. The experimental data consists of full-field measurements from a family of tested mechanical structures. In this framework, a general data-driven approach is proposed to learn the constitutive model (in terms of thermodynamic potentials) from data. A key element that defines the proposed data-driven approach is a tool: the Constitutive Relation Error (CRE); the data-driven model is then the minimizer of the CRE. A notable aspect of this procedure is that it leads to quasi-explicit formulations of the optimal constitutive model. Eventually, a modified Constitutive Relation Error is introduced to take measurement noise into account.

Pierre Ladevèze

Ludovic Chamoin

Data-driven material modeling based on the Constitutive Relation Error

基于本构关系误差的数据驱动材料建模

Abstract This study introduces a surrogate modeling framework merging proper orthogonal decomposition, long short-term memory networks, and multi-task learning, to accurately predict elastoplastic deformations in real-time. Superior to single-task neural networks, this approach achieves a mean absolute error below 0.40% across various state variables, with the multi-task model showing enhanced generalization by mitigating overfitting through shared layers. Moreover, in our use cases, a pre-trained multi-task model can effectively train additional variables with as few as 20 samples, demonstrating its deep understanding of complex scenarios. This is notably efficient compared to single-task models, which typically require around 100 samples. Significantly faster than traditional finite element analysis, our model accelerates computations by approximately a million times, making it a substantial advancement for real-time predictive modeling in engineering applications. While it necessitates further testing on more intricate models, this framework shows substantial promise in elevating both efficiency and accuracy in engineering applications, particularly for real-time scenarios.

R. Schmeitz

J. J. C. Remmers

O. Mula

O. van der Sluis

Towards a real-time simulation of elastoplastic deformation using multi-task neural networks

基于多任务神经网络的弹塑性变形实时仿真研究

Abstract An efficient parallel implementation of the phase field method for quasi-brittle crack simulations able to run on supercomputers with a large number of processes is proposed. This framework uses the finite-element method on 3D structured meshes, and was developed for distributed memory machines using the Message Passing Interface (MPI) for workload distribution and data communication between processes. Parallel assembly is carried out to build the matrices associated with the linear systems of equations. In the proposed context, linear systems derived from displacement and damage discretizations are solved using parallel solvers and preconditioners from the PETSc (Portable, Extensible Toolkit for Scientific Computation) library. All additional operations in this implementation are also efficiently parallelized. Performance analysis shows linear acceleration with an efficiency of $$97\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>97</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for a computation on 6400 processes and over $$80\%$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mn>80</mml:mn> <mml:mo>%</mml:mo> </mml:mrow> </mml:math> for a computation on 10240 processes. The linear systems involved in the simulations with up to $$10^{10}$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:msup> <mml:mn>10</mml:mn> <mml:mn>10</mml:mn> </mml:msup> </mml:math> degrees of freedom can be solved in a few seconds. The methodology is applied to quasi-brittle simulations, involving alternate solving of large linear systems and incremental evolution. The applications presented to illustrate the parallel framework involve crack initiation and propagation in strongly heterogeneous materials with a detailed description of the microstructure. Large three-dimensional periodic structures and a realistic geometrical model directly obtained by micro-CT imagery are used.

Zakaria Chafia

Julien Yvonnet

Jérémy Bleyer

Stéphane Vincent

Simon El Ouafa

Massively parallel phase field fracture simulations on supercomputers: towards multi-billion degree-of-freedom computations

大规模并行相场断裂模拟在超级计算机上的应用：迈向数十亿自由度计算

Abstract In this paper, we present a computationally efficient reduced order model for obtaining blood perfusion profiles within parametric functional units of the liver called ‘lobules’. We consider Darcy’s equation in two-dimensional hexagonal lobule domains with six flow inlets and one outlet, whose positions are parameterized to represent varying lobule geometries. To avoid the meshing effort for every new lobule domain, we map the parametric domain onto a single reference domain. By making use of the contra-variant Piola mapping, we represent solutions of the parametric domains in the reference domain. We then construct a reduced order model via proper orthogonal decomposition (POD). Additionally, we employ the discrete empirical interpolation method (DEIM) to treat the non-affine parameter dependence that appears due to the geometric mapping. For sampling random shapes and sizes of lobules, we generate Voronoi diagrams (VD) from Delaunay triangulations and use an energy minimization problem to control the packing of the lobule structures. To reduce the dimension of the parameterized problem, we exploit the mesh symmetry of the full lobule domain to split the full domain into six rotationally symmetric subdomains. We then use the same set of reduced order basis (ROB) functions within each subdomain for the construction of the reduced order model. We close our study by a thorough investigation of the accuracy and computational efficiency of the resulting reduced order model.

Ahsan Ali Siddiqui

Etienne Jessen

Stein K. F. Stoter

David Néron

Dominik Schillinger

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

参数化多片肝小叶血流灌注的降阶建模

Abstract Two approaches are presented to improve the capabilities of machine learning models in multiscale modeling for microstructure homogenization (graphical abstract in Fig. 1). The first approach features a Bayesian data mining scheme with a human in the loop, halving the prediction error compared to [1] using four novel and efficient to evaluate feature descriptors. The second purely machine learning-driven approach utilizes convolutional neural networks, where we introduce a novel module (the deep inception module) designed to capture characteristics of different length scales within the image. The new module features a new normalization block, which aids in calibrating the differently obtained feature characteristics. Further improvements, universally applicable to artificial neural networks, are found with a novel hyperparameter insensitive learning rate schedule, which adapts to the training progress of the model. A further improvement is given by a pre-trained feature bypass which utilizes global low-level features to serve as baseline prediction such that the model is able to dedicate its attention to high-level features. The proposed schemes have been applied to different literature models, yielding significant improvements in any of the investigated convolutional neural networks. The improvements found by the two overarching contributions, i.e., derived through feature development with a human in the loop, and via convolutional neural networks, are critically assessed in a thermal and mechanical setting. It is further expanded to variable material parameters while allowing for variable microstructural elements, yielding drastically reduced prediction errors across the board.

Abbreviation	Adv. Model. and Simul. in Eng. Sci.
Journal Impact	1.83
Quartiles(Global)	MECHANICS(Q3)

ISSN	2213-7467
h-index	24

Publication Information	Publisher: SpringerOpen，Publishing cycle: ，Journal Type: journal，Open Access Journals: Yes
Basic data	Year of publication: 2014，Proportion of original research papers: 0.00%，Self Citation Rate:0.00%， Gold OA Rate: 100.00%
Journal Fees	$1790Details
Average review cycle	网友分享经验：30 Weeks

Advanced Modeling and Simulation in Engineering Sciences

Journal Citation Format

A journal article with 1 author

A journal article with 2 authors

A journal article with 3 authors

A journal article with 5 or more authors

Books Citation Format

Thesis Citation Format

Web sites Citation Format

Patent Citation Format

Staying up late manually editing references? ivySCI automatically matches journals and helps you generate references with a single click.

Share Submission Experience

Data-driven material modeling based on the Constitutive Relation Error

Towards a real-time simulation of elastoplastic deformation using multi-task neural networks

Massively parallel phase field fracture simulations on supercomputers: towards multi-billion degree-of-freedom computations

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Microstructure homogenization: human vs machine

Massively parallel phase field fracture simulations on supercomputers: towards multi-billion degree-of-freedom computations

Towards a real-time simulation of elastoplastic deformation using multi-task neural networks

Data-driven material modeling based on the Constitutive Relation Error

Reduced order modeling of blood perfusion in parametric multipatch liver lobules

Microstructure homogenization: human vs machine