4 citations · 18 across the 10 of their papers we have counts for
10 papers
Comparison of two artificial neural networks trained for the surrogate modeling of stress in materially heterogeneous elastoplastic solids
Sarthak Kapoor, Jaber Rezaei Mianroodi, Mohammad Khorrami +2
The purpose of this work is the systematic comparison of the application of two artificial neural networks (ANNs) to the surrogate modeling of the stress field in materially hetero…
Computational Discovery of Energy-Efficient Heat Treatment for Microstructure Design using Deep Reinforcement Learning
Jaber R. Mianroodi, Nima H. Siboni, Dierk Raabe
Deep Reinforcement Learning (DRL) is employed to develop autonomously optimized and custom-designed heat-treatment processes that are both, microstructure-sensitive and energy effi…
Accelerating phase-field-based simulation via machine learning
Iman Peivaste, Nima H. Siboni, Ghasem Alahyarizadeh +4
Phase-field-based models have become common in material science, mechanics, physics, biology, chemistry, and engineering for the simulation of microstructure evolution. Yet, they s…
Phase-Field Modeling of Coupled Brittle-Ductile Fracture in Aluminum Alloys
Samad Vakili, Pratheek Shanthraj, Franz Roters +2
Fracture in aluminum alloys with precipitates involves at least two mechanisms, namely, ductile fracture of the aluminum-rich matrix and brittle fracture of the precipitates. In th…
Hierarchical nature of hydrogen-based direct reduction of iron oxides
Yan Ma, Isnaldi R. Souza Filho, Yang Bai +12
Fossil-free ironmaking is indispensable for reducing massive anthropogenic CO2 emissions in the steel industry. Hydrogen-based direct reduction (HyDR) is among the most attractive…
Lossless Multi-Scale Constitutive Elastic Relations with Artificial Intelligence
Jaber Rezaei Mianroodi, Shahed Rezaei, Nima H. Siboni +2
The elastic properties of materials derive from their electronic and atomic nature. However, simulating bulk materials fully at these scales is not feasible, so that typically homo…