Imagen de Portada

Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning

Fuente: arXiv

Guardado en:

Detalles Bibliográficos
Autores principales:	Suzuki, Misato, Shizawa, Kazuyuki, Muramatsu, Mayu
Formato:	Preprint
Publicado:	2024
Materias:	Computational Engineering, Finance, and Science
Acceso en línea:	Acceder al recurso
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

Ejemplares similares

Dislocation-based crystal plasticity simulation on grain-size dependence of mechanical properties in dual-phase steels
por: Suzuki, Misato, et al.
Publicado: (2024)

Conditional diffusion model for inverse prediction of process parameters and dendritic microstructures from mechanical properties
por: Ikeda, Arisa, et al.
Publicado: (2024)

Adaptive Encoding Strategy for Quantum Annealing in Mixed-Variable Engineering Optimization
por: Key, Fabian, et al.
Publicado: (2026)

An Ising Machine Formulation for Design Updates in Topology Optimization of Flow Channels
por: Suzuki, Yudai, et al.
Publicado: (2024)

A variational multi-phase model for elastoplastic materials with microstructure evolution
por: Dinkelacker-Steinhoff, Sarah, et al.
Publicado: (2026)

Automated machine learning for physics-informed convolutional neural networks
por: Zhou, Wanyun, et al.
Publicado: (2024)

Lightweight equivariant model for efficient machine learning interatomic potentials
por: Yang, Ziduo, et al.
Publicado: (2023)

Textual semantics and machine learning methods for data product pricing
por: Gao, Ruize, et al.
Publicado: (2025)

A highly efficient computational approach for part-scale microstructure predictions in Ti-6Al-4V additive manufacturing
por: Proell, Sebastian D., et al.
Publicado: (2024)

Cohesive phase-field fracture with an explicit strength surface: an eigenstrain-based return-mapping formulation
por: Hageman, Tim
Publicado: (2026)

Transforming physics-informed machine learning to convex optimization
por: Yi, Letian, et al.
Publicado: (2025)

Rapid modelling of reactive transport in porous media using machine learning: limitations and solutions
por: Silva, Vinicius L S, et al.
Publicado: (2024)

A differentiable structural analysis framework for high-performance design optimization
por: Lee, Keith J., et al.
Publicado: (2024)

Towards reproducible machine learning-based process monitoring and quality prediction research for additive manufacturing
por: Xie, Jiarui, et al.
Publicado: (2024)

Identification of microstructure from macroscopic measurement using inverse multiscale analysis
por: Mukherjee, Anjan, et al.
Publicado: (2024)

Inverse design of anisotropic microstructures using physics-augmented neural networks
por: Jadoon, Asghar A., et al.
Publicado: (2024)

Interpretable domain knowledge enhanced machine learning framework on axial capacity prediction of circular CFST columns
por: Wang, Dian, et al.
Publicado: (2024)

Crack detection by holomorphic neural networks and transfer-learning-enhanced genetic optimization
por: Hund, Jonas, et al.
Publicado: (2025)

GRFsaw: A lightweight stochastic microstructure generator
por: Blatny, Lars, et al.
Publicado: (2024)

Comparative analysis of phase-field and intrinsic cohesive zone models for fracture simulations in multiphase materials with interfaces: Investigation of the influence of the microstructure on the fracture properties
por: Koopas, Rasoul Najafi, et al.
Publicado: (2023)

Physics-informed machine learning surrogate for scalable simulation of thermal histories during wire-arc directed energy deposition
por: Ryan, Michael, et al.
Publicado: (2025)

Defining metric-aware size-shape measures to validate and optimize curved high-order meshes
por: Aparicio-Estrems, Guillermo, et al.
Publicado: (2024)

GrainGNN: A dynamic graph neural network for predicting 3D grain microstructure
por: Qin, Yigong, et al.
Publicado: (2024)

Style-constrained inverse design of microstructures with tailored mechanical properties using unconditional diffusion models
por: Xu, Weipeng, et al.
Publicado: (2026)

Towards a sharper phase-field method: a hybrid diffuse-semisharp approach for microstructure evolution problems
por: Dobrzanski, Jedrzej, et al.
Publicado: (2024)

A globalized and preconditioned Newton-CG solver for metric-aware curved high-order mesh optimization
por: Aparicio-Estrems, Guillermo, et al.
Publicado: (2024)

A plastic correction algorithm for full-field elasto-plastic finite element simulations : critical assessment of predictive capabilities and improvement by machine learning
por: Palchoudhary, Abhishek, et al.
Publicado: (2024)

Adaptive optimization of wave energy conversion in oscillatory wave surge converters via SPH simulation and deep reinforcement learning
por: Ye, Mai, et al.
Publicado: (2024)

Accurate and efficient predictions of keyhole dynamics in laser materials processing using machine learning-aided simulations
por: Zhang, Jiahui, et al.
Publicado: (2024)

Thermodynamically consistent machine learning model for excess Gibbs energy
por: Hoffmann, Marco, et al.
Publicado: (2025)

Multi-objective free-form shape optimization of a synchronous reluctance machine
por: Gangl, Peter, et al.
Publicado: (2020)

Balancing the exploration-exploitation trade-off in active learning for surrogate model-based reliability analysis via multi-objective optimization
por: Moran, Jonathan A., et al.
Publicado: (2025)

Learning to optimize convex risk measures: The cases of utility-based shortfall risk and optimized certainty equivalent risk
por: Gupte, Sumedh, et al.
Publicado: (2025)

Multifidelity linear regression for scientific machine learning from scarce data
por: Qian, Elizabeth, et al.
Publicado: (2024)

Introducing a microstructure-embedded autoencoder approach for reconstructing high-resolution solution field data from a reduced parametric space
por: Koopas, Rasoul Najafi, et al.
Publicado: (2024)

Vertex and front-tracking methods for the modeling of microstructure evolution at the solid state: a brief review
por: Bernacki, Marc
Publicado: (2025)

A spatiotemporal deep learning framework for prediction of crack dynamics in heterogeneous solids: efficient mapping of concrete microstructures to its fracture properties
por: Koopas, Rasoul Najafi, et al.
Publicado: (2024)

Beyond development: Challenges in deploying machine learning models for structural engineering applications
por: Esteghamati, Mohsen Zaker, et al.
Publicado: (2024)

Quotient complex (QC)-based machine learning for 2D perovskite design
por: Hu, Chuan-Shen, et al.
Publicado: (2024)

MLPROP -- an open interactive web interface for thermophysical property prediction with machine learning
por: Hoffmann, Marco, et al.
Publicado: (2025)