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.

Lißner Julian

Fritzen Felix

Advanced Modeling and Simulation in Engineering Sciences

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。

Microstructure homogenization: human vs machine

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Journal Info

Category	Quartile
MECHANICS	3