26 citations · 31 across the 4 of their papers we have counts for
4 papers
Bridging Phase-Field Model and Deep Learning for Predicting 2D and 3D Microstructure Evolution in Ternary Alloys
Owais Ahmad, Aravind K, Naveen Kumar +3
We develop a hybrid framework that integrates a phase-field model (PFM) with an attention-enhanced deep learning (DL) architecture to study ternary spinodal dealloying, a sophistic…
Deep Learning-Driven Prediction of Microstructure Evolution via Latent Space Interpolation
Sachin Gaikwad, Thejas Kasilingam, Owais Ahmad +2
Phase-field models accurately simulate microstructure evolution, but their dependence on solving complex differential equations makes them computationally expensive. This work achi…
Microstructural Studies Using Generative Adversarial Network (GAN): a Case Study
Owais Ahmad, Vishal Panwar, Kaushik Das +2
The generative adversarial network (GAN) is one of the most widely used deep generative models for synthesizing high-quality images with the same statistics as the training set. Fi…
Accelerating microstructure modelling via machine learning: a new method combining Autoencoder and ConvLSTM
Owais Ahmad, Naveen Kumar, Rajdip Mukherjee +1
Phase-field modeling is an elegant and versatile computation tool to predict microstructure evolution in materials in the mesoscale regime. However, these simulations require rigor…