Differentiable finite element method with Galerkin discretization for fast and accurate inverse analysis of multidimensional heterogeneous engineering structures

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2025, Номер 437, С. 117755 - 117755

Опубликована: Янв. 22, 2025

Язык: Английский

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Physics and data hybrid-driven interpretable deep learning for moving force identification

Engineering Structures, Год журнала: 2025, Номер 329, С. 119801 - 119801

Опубликована: Фев. 3, 2025

Язык: Английский

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Structural nonlinear boundary condition identification using a hybrid physics data-driven approach

Nonlinear Dynamics, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 23, 2024

Abstract As civil infrastructures often exhibit nonlinearities, the identification of nonlinear behaviors is crucial to assess structural safety state. However, existing physics-driven methods can only estimate parameters given a known behavior pattern. By contrast, data-driven merely map load-response relationship at level, rather than identify an accurate mapping component level. To address these issues, hybrid physics-data-driven strategy developed in this study blind nonlinearity. The components are surrogated by multilayer perceptron, and linear ones simulated using finite element method. Subsequently, global stiffness matrix restoring force vector assembled according elemental topology obtain model. discrepancy between measured model-predicted responses formulated as loss function, minimizing which MLPs indirectly trained nonlinearities be identified without knowing nonlinearity type. Three numerical cases used verify proposed method identifying elastic, hysteretic, multiple boundary conditions. Results show that robust different noise levels, sensor placements, types. Moreover, model possesses strong generalization ability accurately predict full-field responses.

Язык: Английский

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Solving fluid flow in discontinuous heterogeneous porous media and multi-layer strata with interpretable physics-encoded finite element network

Journal of Rock Mechanics and Geotechnical Engineering, Год журнала: 2024, Номер unknown

Опубликована: Дек. 1, 2024

Язык: Английский

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A Comprehensive Investigation of Physics-Informed Learning in Forward and Inverse Analysis of Elastic and Elastoplastic Footing

Computers and Geotechnics, Год журнала: 2025, Номер 181, С. 107110 - 107110

Опубликована: Фев. 5, 2025

Язык: Английский

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Explainable artificial intelligence model for the prediction of undrained shear strength

Theoretical and Applied Mechanics Letters, Год журнала: 2025, Номер unknown, С. 100578 - 100578

Опубликована: Фев. 1, 2025

Язык: Английский

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State-of-art review on load identification and response reconstruction to realize digital twin of infrastructures

Advances in Structural Engineering, Год журнала: 2025, Номер unknown

Опубликована: Март 20, 2025

The digital twin (DT) technique for infrastructures has been developed and attracted a significant amount of attention since 2020. Nonetheless, the key technologies used DT, including load identification (LID), response reconstruction (RRE), damage detection, have much longer history than DT itself. By employing these methods, cyber models are established updated to represent operational state real structure, meanwhile, monitored data at discrete locations can be expanded full-field structure realize DT. In this work, LID RRE methods civil under quasi-static dynamic loading actions comprehensively reviewed. LID, four types formulations derived, solutions summarized address inherent ill-posed problems. RRE, model- data-driven reviewed with five levels performance. Subsequently, several sensing techniques introduced. pros, cons, features highlighted. challenges prospects in outline future trend in-service infrastructure digitalization.

Язык: Английский

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Physics-informed neural networks for solving steady-state temperature field in artificial ground freezing

Canadian Geotechnical Journal, Год журнала: 2025, Номер 62, С. 1 - 17

Опубликована: Янв. 1, 2025

Artificial ground freezing (AGF) is a widely used technique for soil stabilization and waterproofing. Numerous studies have been devoted to solving the heat transfer problems in AGF while encountering limitations handling complex geometries boundary conditions being computationally intensive. Recently, using machine learning methods predict temperature fields has gained attention, demonstrating potential achieve higher accuracy than conventional models. However, these are typically limited by need large, labeled datasets, which time-consuming difficult obtain. In this study, we address challenges applying physics-informed neural networks (PINNs) solve steady-state problem AGF, focusing on distribution around single pipe. By embedding conduction equation into loss function, PINNs reduce extensive data. To enhance efficiency, employed, results compared against finite element method. Results show that high accuracy, particularly larger domains with moderate gradients, providing competitive performance more configurations involving steeper gradients. This approach offers promising alternative modeling geotechnical applications, implications reducing computational costs design.

Язык: Английский

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The novel graph transformer-based surrogate model for learning physical systems

Computer Methods in Applied Mechanics and Engineering, Год журнала: 2024, Номер 432, С. 117410 - 117410

Опубликована: Окт. 2, 2024

Язык: Английский

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Application of a thermo-mechanical-damage coupling model based on the TLF-SPH in the thermal cracking simulation of brittle solids

Computers and Geotechnics, Год журнала: 2024, Номер 179, С. 107017 - 107017

Опубликована: Дек. 26, 2024

Язык: Английский

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