29 citations · 58 across the 18 of their papers we have counts for
18 papers
An approach to encode divergence-free stress fields in neural approximations based on stress potentials
Mohammad S. Khorrami, Pawan Goyal, Soroush Motahari +5
The purpose of the current work is the development of an approach to account for quasi-static mechanical equilibrium in empirical (i.e., data-based) models for the stress field emp…
A physics-encoded Fourier neural operator approach for surrogate modeling of divergence-free stress fields in solids
Mohammad S. Khorrami, Pawan Goyal, Jaber R. Mianroodi +3
The purpose of the current work is the development of a so-called physics-encoded Fourier neural operator (PeFNO) for surrogate modeling of the quasi-static equilibrium stress fiel…
Roadmap on Data-Centric Materials Science
Stefan Bauer, Peter Benner, Tristan Bereau +58
Science is and always has been based on data, but the terms "data-centric" and the "4th paradigm of" materials research indicate a radical change in how information is retrieved, h…
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…
Effect of Pore Formation on Redox-Driven Phase Transformation
Xuyang Zhou, Yang Bai, Ayman A. El-Zoka +7
When solid-state redox-driven phase transformations are associated with mass loss, vacancies are produced that develop into pores. These pores can influence the kinetics of certain…
An artificial neural network for surrogate modeling of stress fields in viscoplastic polycrystalline materials
Mohammad S. Khorrami, Jaber R. Mianroodi, Nima H. Siboni +4
The purpose of this work is the development of an artificial neural network (ANN) for surrogate modeling of the mechanical response of viscoplastic grain microstructures. To this e…
Combined modeling and experimental characterization of Mn segregation and spinodal decomposition along dislocation lines in Fe-Mn alloys
Jaber Rezaei Mianroodi, Pratheek Shanthraj, Alisson Kwiatkowski da Silva +2
Mn enrichment at dislocations in Fe-Mn alloys due to segregation and spinodal decomposition along the dislocation line is studied via modeling and experimental characterization. To…
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…
On the higher-order pseudo-continuum characterization of discrete kinematic results from experimental measurement or discrete simulation
Mohammad Khorrami, Jaber R. Mianroodi, Bob Svendsen
The purpose of this work is the development and determination of higher-order continuum-like kinematic measures which characterize discrete kinematic data obtained from experimenta…
Chemo-Mechanical Phase-Field Modeling of Iron Oxide Reduction with Hydrogen
Yang Bai, Jaber Rezaei Mianroodi, Yan Ma +3
The reduction of iron ore with carbon-carriers is one of the largest sources of greenhouse gas emissions in the industry, motivating global activities to replace the coke-based bla…
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…
Teaching Solid Mechanics to Artificial Intelligence: a fast solver for heterogeneous solids
Jaber Rezaei Mianroodi, Nima H. Siboni, Dierk Raabe
We propose a deep neural network (DNN) as a fast surrogate model for local stress (and in principle strain) calculation in inhomogeneous non-linear material systems. We show that t…
Influence of microstructure and atomic-scale chemistry on iron ore reduction with hydrogen at 700°C
Se-Ho Kim, Xue Zhang, Kevin Schweinar +10
With 1.85 billion tons produced per year, steel is the most important material class in terms of volume and environmental impact. While steel is a sustainability enabler, for insta…
Development and comparison of spectral algorithms for numerical modeling of the quasi-static mechanical behavior of inhomogeneous materials
M. Khorrami, J. R. Mianroodi, P. Shanthraj +1
In the current work, a number of algorithms are developed and compared for the numerical solution of periodic (quasi-static) linear elastic mechanical boundary-value problems (BVPs…
Alloy Design for Mechanical Properties: Conquering the Length Scales
Irene J. Beyerlein, Shuozhi Xu, Javier LLorca +3
Predicting the structural response of advanced multiphase alloys and understanding the underlying microscopic mechanisms that are responsible for it are two critically important ro…