2 citations · 4 across the 6 of their papers we have counts for
4 papers · 1 filter
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…
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…