Novel DeepONet architecture to predict stresses in elastoplastic structures with variable complex geometries and loads

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2023, Номер 415, С. 116277 - 116277

Опубликована: Июль 28, 2023

Язык: Английский

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Sequential Deep Operator Networks (S-DeepONet) for predicting full-field solutions under time-dependent loads

Engineering Applications of Artificial Intelligence, Год журнала: 2023, Номер 127, С. 107258 - 107258

Опубликована: Окт. 11, 2023

Язык: Английский

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Recent Advances in Machine Learning‐Assisted Multiscale Design of Energy Materials

Advanced Energy Materials, Год журнала: 2024, Номер unknown

Опубликована: Дек. 10, 2024

Abstract This review highlights recent advances in machine learning (ML)‐assisted design of energy materials. Initially, ML algorithms were successfully applied to screen materials databases by establishing complex relationships between atomic structures and their resulting properties, thus accelerating the identification candidates with desirable properties. Recently, development highly accurate interatomic potentials generative models has not only improved robust prediction physical but also significantly accelerated discovery In past couple years, methods have enabled high‐precision first‐principles predictions electronic optical properties for large systems, providing unprecedented opportunities science. Furthermore, ML‐assisted microstructure reconstruction physics‐informed solutions partial differential equations facilitated understanding microstructure–property relationships. Most recently, seamless integration various platforms led emergence autonomous laboratories that combine quantum mechanical calculations, language models, experimental validations, fundamentally transforming traditional approach novel synthesis. While highlighting aforementioned advances, existing challenges are discussed. Ultimately, is expected fully integrate atomic‐scale simulations, reverse engineering, process optimization, device fabrication, empowering system design. will drive transformative innovations conversion, storage, harvesting technologies.

Язык: Английский

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Methods for enabling real-time analysis in digital twins: A literature review

Computers & Structures, Год журнала: 2024, Номер 297, С. 107342 - 107342

Опубликована: Апрель 4, 2024

This paper presents a literature review on methods for enabling real-time analysis in digital twins, which are virtual models of physical systems. The advantages twins numerous, including cost reduction, risk mitigation, efficiency enhancement, and decision-making support. However, their implementation faces challenges such as the need data analysis, resource limitations, uncertainty. focuses reducing computational demands, have not been systematically discussed literature. reviews categorizes tools accelerating modeling phenomena needs twins.

Язык: Английский

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Transfer Learning-Enhanced Finite Element-Integrated Neural Networks

International Journal of Mechanical Sciences, Год журнала: 2025, Номер unknown, С. 110075 - 110075

Опубликована: Фев. 1, 2025

Язык: Английский

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Learning solutions of thermodynamics-based nonlinear constitutive material models using physics-informed neural networks

Computational Mechanics, Год журнала: 2024, Номер 74(2), С. 333 - 366

Опубликована: Янв. 9, 2024

Язык: Английский

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Physics-Informed neural network solver for numerical analysis in geoengineering

Georisk Assessment and Management of Risk for Engineered Systems and Geohazards, Год журнала: 2024, Номер 18(1), С. 33 - 51

Опубликована: Янв. 2, 2024

Engineering-scale problems generally can be described by partial differential equations (PDEs) or ordinary (ODEs). Analytical, semi-analytical and numerical analysis are commonly used for deriving the solutions of such PDEs/ODEs. Recently, a novel physics-informed neural network (PINN) solver has emerged as promising alternative to solve PINN resembles mesh-free method which leverages strong non-linear ability deep learning algorithms (e.g. networks) automatically search correct spatial-temporal responses constrained embedded This study comprehensively reviews current state including its principles forward inverse problems, baseline PINN, enhanced variants combined with special sampling strategies loss functions. shows an easier modelling process superior feasibility compared conventional methods. Meanwhile, limitations challenges applications solvers constitutive multi-scale/phase also discussed in terms convergence computational costs. exhibited huge potential geoengineering brings revolutionary way numerous domain problems.

Язык: Английский

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Finite element-integrated neural network framework for elastic and elastoplastic solids

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 433, С. 117474 - 117474

Опубликована: Ноя. 1, 2024

Язык: Английский

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Interpretable physics-encoded finite element network to handle concentration features and multi-material heterogeneity in hyperelasticity

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 431, С. 117268 - 117268

Опубликована: Авг. 8, 2024

Язык: Английский

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A physics-informed neural network framework for laminated composite plates under bending

Thin-Walled Structures, Год журнала: 2025, Номер unknown, С. 113014 - 113014

Опубликована: Янв. 1, 2025

Язык: Английский

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