41 citations · 59 across the 5 of their papers we have counts for
5 papers
Coupled Diffusion-Encoder Models for Reconstruction of Flow Fields
AmirPouya Hemmasian, Amir Barati Farimani
Data-driven flow-field reconstruction typically relies on autoencoder architectures that compress high-dimensional states into low-dimensional latent representations. However, clas…
Pretraining a Neural Operator in Lower Dimensions
AmirPouya Hemmasian, Amir Barati Farimani
There has recently been increasing attention towards developing foundational neural Partial Differential Equation (PDE) solvers and neural operators through large-scale pretraining…
Strategies for Pretraining Neural Operators
Anthony Zhou, Cooper Lorsung, AmirPouya Hemmasian +1
Pretraining for partial differential equation (PDE) modeling has recently shown promise in scaling neural operators across datasets to improve generalizability and performance. Des…
Multi-scale Time-stepping of Partial Differential Equations with Transformers
AmirPouya Hemmasian, Amir Barati Farimani
Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design and optimization in almost all scientific and engineering applications. Neural networks…
Surrogate Modeling of Melt Pool Thermal Field using Deep Learning
AmirPouya Hemmasian, Francis Ogoke, Parand Akbari +3
Powder-based additive manufacturing has transformed the manufacturing industry over the last decade. In Laser Powder Bed Fusion, a specific part is built in an iterative manner in…