Ta-Te Chen

Assistant Professor

Nagoya University

Japan

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About

Ta-Te Chen is an Assistant Professor at Nagoya University in Japan. His research focuses on materials science, specifically examining the mechanical properties of alloys and steels through innovative techniques such as machine learning and optimization methods. Recent publications include studies on the strength-ductility balance in dual-phase steels and the application of generative adversarial networks in material design.

Articles (16)

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2025

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2025

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2025

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2025

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2024

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2023

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2023

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2023

Collaborators (4)

Xiaoyang Zheng

Assistant professor

University of Tokyo

JAPAN

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Ikumu Watanabe

National Institute for Materials Science

JAPAN

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Yoshitaka Adachi

Nagoya University

JAPAN

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Fei Sun

Specially Appointed Lecturer

Nagoya University

JAPAN

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Ta-Te Chen

Research Interests

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About

Articles (16)

Negative enthalpy-induced phase decomposition and transformation in Fe-35Ni-10Al medium entropy alloy

Improvement of Strength–Ductility Balance in Tempered Martensite Steel Using Multi-Objective Bayesian Optimization

A data-driven method for estimating the plastic properties of alloys through Vickers hardness tests

Dramatic improvement in strength–ductility balance of dual-phase steels by optimizing features of ferrite phase

Comparative study of the experimentally observed and GAN-generated 3D microstructures in dual-phase steels

Multi-objective topology optimization of porous microstructure in die-bonding layer of a semiconductor

Maximization of strength–ductility balance of dual-phase steels using generative adversarial networks and Bayesian optimization

Machine Learning-Aided Analysis of the Rolling and Recrystallization Textures of Pure Iron with Different Cold Reduction Ratios and Cold-Rolling Directions

Analysis of the strength–ductility balance of dual-phase steel using a combination of generative adversarial networks and finite element method

Image regression analysis for linking the microstructure and property of steel

Minimal-surface-based multiphase metamaterials with highly variable stiffness

Cellular automaton simulation of solid-phase grain growth under conditions involving scanning heat sources and temperature gradients

A review on inverse analysis models in steel material design

Deep-learning-based inverse design of three-dimensional architected cellular materials with the target porosity and stiffness using voxelized Voronoi lattices

Two- and Three-Dimensional Modeling and Simulations of Grain Growth Behavior in Dual-Phase Steel Using Monte Carlo and Machine Learning

Simulation of Abnormal Grain Growth Using the Cellular Automaton Method

Collaborators (4)

Xiaoyang Zheng

Ikumu Watanabe

Yoshitaka Adachi

Fei Sun

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