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Massimiliano Lupo Pasini portrait

Massimiliano Lupo Pasini

Data Scientist

Contact

LUPOPASINIM@ORNL.GOV

All Publications

Study of solid molecular deuterium D2 growth under gas pressure
Stable parallel training of Wasserstein conditional generative adversarial neural networks
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks
Anderson Acceleration for Distributed Training of Deep Learning Models...
Hierarchical Model Reduction Driven by a Proper Orthogonal Decomposition for Parametrized Advection-Diffusion-Reaction Problems
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence
Scalable balanced training of conditional generative adversarial neural networks on image data
A scalable algorithm for the optimization of neural network architectures
Fast and stable deep-learning predictions of material properties for solid solution alloys
Benchmark of the LAMMPS code on CRESCO and SUMMIT HPC systems
A Parallel Strategy for Density Functional Theory Computations on Accelerated Nodes...
Convergence analysis of Anderson‐type acceleration of Richardson's iteration

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Organizations

Computing and Computational Sciences Directorate
Computational Sciences and Engineering Division
Advanced Computing Methods for Engineered Systems Section
Computational Coupled Physics

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Lupo Pasini receives NERSC Early Career Achievement Award

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Researchers at ORNL demonstrate scalability of GNNs on world’s most powerful computing systems
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