440 citations · 851 across the 27 of their papers we have counts for
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cond-mat.mtrl-sci2022★ 6 cited
Learning two-phase microstructure evolution using neural operators and autoencoder architectures
Vivek Oommen, Khemraj Shukla, Somdatta Goswami +2
Phase-field modeling is an effective but computationally expensive method for capturing the mesoscale morphological and microstructure evolution in materials. Hence, fast and gener…
cond-mat.mtrl-sci2021★ 124 cited
A physics-informed neural network for quantifying the microstructure properties of polycrystalline Nickel using ultrasound data
Khemraj Shukla, Ameya D. Jagtap, James L. Blackshire +2
We employ physics-informed neural networks (PINNs) to quantify the microstructure of a polycrystalline Nickel by computing the spatial variation of compliance coefficients (compres…