Physics-informed data assimilation model for displacement prediction of hydrodynamic pressure-driven landslide

Computers and Geotechnics, Journal Year: 2024, Volume and Issue: 167, P. 106085 - 106085

Published: Jan. 16, 2024

Language: Английский

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Machine learning-based constitutive modelling for material non-linearity: A review

Mechanics of Advanced Materials and Structures, Journal Year: 2024, Volume and Issue: unknown, P. 1 - 19

Published: Dec. 17, 2024

Machine learning (ML) models are widely used across numerous scientific and engineering disciplines due to their exceptional performance, flexibility, prediction quality, ability handle highly complex problems if appropriate data available. One example of such areas which has attracted a lot attentions in the last couple years is integration data-driven approaches material modeling. There been several successful researches implementing ML-based constitutive instead classical phenomenological for various materials, particularly those with non-linear mechanical behaviors. This review paper aims systematically investigate literature on materials classify these based suitability non-linearity including Non-linear elasticity (hyperelasticity), plasticity, visco-elasticity, visco-plasticity. Furthermore, we also reviewed compared that have applied architectured as groups designed represent specific behaviors might not exist conventional categories. The other goal this provide initial steps understanding modeling, artificial neural networks (ANN), Gaussian processes, random forests (RF), generated adversarial (GANs), support vector machines (SVM), different regression physics-informed (PINN). outlines collection methods, types data, processing approaches, theoretical background ML models, advantage limitations potential future research directions. comprehensive will researchers knowledge necessary develop high-fidelity, robust, adaptable, flexible, accurate advanced materials.

Language: Английский

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Incremental Neural Controlled Differential Equations for modeling of path-dependent material behavior

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 422, P. 116789 - 116789

Published: Feb. 14, 2024

Language: Английский

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A path-dependence aware LSTM-based framework for modeling the mechanical behavior of unsaturated soil

Computers and Geotechnics, Journal Year: 2025, Volume and Issue: 179, P. 107060 - 107060

Published: Jan. 9, 2025

Language: Английский

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Parameter inverse analysis of high rockfill dams considering material uncertainty based on the EJaya-SESM model

Advanced Engineering Informatics, Journal Year: 2025, Volume and Issue: 65, P. 103306 - 103306

Published: April 7, 2025

Language: Английский

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A machine learning based multi-scale finite element framework for nonlinear composite materials

Engineering With Computers, Journal Year: 2025, Volume and Issue: unknown

Published: April 7, 2025

Language: Английский

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Data-driven mechanical behavior modeling of granular biomass materials

Computers and Geotechnics, Journal Year: 2024, Volume and Issue: 177, P. 106907 - 106907

Published: Nov. 20, 2024

Language: Английский

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A deep learning-based crystal plasticity finite element model

Scripta Materialia, Journal Year: 2024, Volume and Issue: 254, P. 116315 - 116315

Published: Aug. 26, 2024

Language: Английский

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N-adaptive ritz method: A neural network enriched partition of unity for boundary value problems

Computer Methods in Applied Mechanics and Engineering, Journal Year: 2024, Volume and Issue: 428, P. 117070 - 117070

Published: June 14, 2024

Language: Английский

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Data-driven modeling of an unsaturated bentonite buffer model test under high temperatures using an enhanced axisymmetric reproducing kernel particle method

Computers and Geotechnics, Journal Year: 2024, Volume and Issue: 168, P. 106133 - 106133

Published: Feb. 8, 2024

In deep geological repositories for high level nuclear waste with close canister spacings, bentonite buffers can experience temperatures higher than 100 °C. this range of extreme temperatures, phenomenological constitutive laws face limitations in capturing the thermo-hydraulic behavior bentonite, since pre-defined functional often lack generality and flexibility to capture a wide complex coupling phenomena as well effects stress state path dependency. work, neural network (DNN)-based soil–water retention curve (SWRC) is introduced integrated into Reproducing Kernel Particle Method (RKPM) conducting simulations buffer. The DNN-SWRC model incorporates temperature an additional input variable, allowing it learn relationship between suction degree saturation under general non-isothermal condition, which difficult represent using SWRC. For effective modeling tank-scale test, new axisymmetric basis functions enriched singular Dirichlet enforcement representing heater placement convective heat transfer coefficient thin-layer composite tank construction are developed. proposed method demonstrated through experiment involving cylindrical layer MX-80 exposed central heating.

Language: Английский

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