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Data-driven surrogate model for predicting the toughness of 3D polycrystals

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Outgoing claims: the papers this work cites, builds on, or argues with.

inferredcites →https://doi.org/10.1002/nme.2861openalex-snapshot

Thermodynamically consistent phase‐field models of fracture: Variational principles and multi‐field FE implementations

inferredcites →https://doi.org/10.1007/s00466-017-1409-0openalex-snapshot

Multi-phase-field modeling of anisotropic crack propagation for polycrystalline materials

inferredcites →https://doi.org/10.1007/s11831-025-10245-4openalex-snapshot

Recent Progress of Digital Reconstruction in Polycrystalline Materials

inferredcites →https://doi.org/10.1016/j.apsusc.2013.03.065openalex-snapshot

First principles study of α2-Ti3Al(0001) surface and γ-TiAl(111)/α2-Ti3Al(0001) interfaces

inferredcites →https://doi.org/10.1016/j.cma.2019.01.016openalex-snapshot

An inverse micro-mechanical analysis toward the stochastic homogenization of nonlinear random composites

inferredcites →https://doi.org/10.1016/j.cma.2024.117689openalex-snapshot

A deep learning framework to predict microstructurally small fatigue crack growth in three-dimensional polycrystals

inferredcites →https://doi.org/10.1016/j.cma.2025.117984openalex-snapshot

Predicting crack nucleation and propagation in brittle materials using Deep Operator Networks with diverse trunk architectures

inferredcites →https://doi.org/10.1016/j.engfracmech.2022.108624openalex-snapshot

Convolutional neural network for predicting crack pattern and stress-crack width curve of air-void structure in 3D printed concrete

inferredcites →https://doi.org/10.1016/j.jcp.2018.10.045openalex-snapshot

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

inferredcites →https://doi.org/10.1016/j.jmps.2022.105188openalex-snapshot

Crack path predictions in heterogeneous media by machine learning

inferredcites →https://doi.org/10.1016/j.tafmec.2019.102447openalex-snapshot

Transfer learning enhanced physics informed neural network for phase-field modeling of fracture

inferredcites →https://doi.org/10.1038/s42254-024-00712-5openalex-snapshot

Neural operators for accelerating scientific simulations and design

inferredcites →https://doi.org/10.1038/s42256-021-00302-5openalex-snapshot

Learning nonlinear operators via DeepONet based on the universal approximation theorem of operators

inferredcites →https://doi.org/10.1073/pnas.1814058116openalex-snapshot

Learning data-driven discretizations for partial differential equations

inferredcites →https://doi.org/10.1088/0022-3727/44/39/395302openalex-snapshot

Influence of material ductility and crack surface roughness on fracture instability

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