Massimiliano Lupo Pasini

According to our database, Massimiliano Lupo Pasini authored at least 28 papers between 2017 and 2024.

Collaborative distances:

• Dijkstra number of three.
• Erdős number of three.

2024

Anderson acceleration with approximate calculations: Applications to scientific computing.

Massimiliano Lupo Pasini, M. Paul Laiu

Numer. Linear Algebra Appl., October, 2024

A Perspective on Scalable AI on High-Performance Computing and Leadership Class Supercomputing Facilities [Industrial and Governmental Activities].

Massimiliano Lupo Pasini

IEEE Comput. Intell. Mag., August, 2024

AI for Materials Design and Discovery Using Atomistic Scale Information [Industrial and Governmental Activities].

Massimiliano Lupo Pasini

IEEE Comput. Intell. Mag., May, 2024

Transferring predictions of formation energy across lattices of increasing size.

Massimiliano Lupo Pasini, Mariia Karabin, Markus Eisenbach

Mach. Learn. Sci. Technol., 2024

Scalable Training of Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN.

Massimiliano Lupo Pasini, Jong Youl Choi, Kshitij Mehta, Pei Zhang, David M. Rogers, Jonghyun Bae, Khaled Z. Ibrahim, Ashwin M. Aji, Karl W. Schulz, Jorda Polo, Prasanna Balaprakash

CoRR, 2024

Scaling Ensembles of Data-Intensive Quantum Chemical Calculations for Millions of Molecules.

Kshitij Mehta, Massimiliano Lupo Pasini, Stephan Irle, Pilsun Yoo, Frédéric Suter, Dmitry Ganyushin, Scott Klasky

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

MDLoader: A Hybrid Model-driven Data Loader for Distributed Deep Neural Networks Training.

Jonghyun Bae, Jong Youl Choi, Massimiliano Lupo Pasini, Kshitij Mehta, Khaled Z. Ibrahim

Proceedings of the IEEE International Parallel and Distributed Processing Symposium, 2024

2023

Stable parallel training of Wasserstein conditional generative adversarial neural networks.

Massimiliano Lupo Pasini, Junqi Yin

J. Supercomput., 2023

Hierarchical Model Reduction Driven by Machine Learning for Parametric Advection-Diffusion-Reaction Problems in the Presence of Noisy Data.

Massimiliano Lupo Pasini, Simona Perotto

J. Sci. Comput., 2023

DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies.

Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang, Conglong Li, Shiyang Chen, Chengming Zhang, Masahiro Tanaka, Xiaoxia Wu, Jeff Rasley, Ammar Ahmad Awan, Connor Holmes, Martin Cai, Adam Ghanem, Zhongzhu Zhou, Yuxiong He, Pete Luferenko, Divya Kumar, Jonathan A. Weyn, Ruixiong Zhang, Sylwester Klocek, Volodymyr Vragov, Mohammed AlQuraishi, Gustaf Ahdritz, Christina Floristean, Cristina Negri, Rao Kotamarthi, Venkatram Vishwanath, Arvind Ramanathan, Sam Foreman, Kyle Hippe, Troy Arcomano, Romit Maulik, Maxim Zvyagin, Alexander Brace, Bin Zhang, Cindy Orozco Bohorquez, Austin Clyde, Bharat Kale, Danilo Perez-Rivera, Heng Ma, Carla M. Mann, Michael W. Irvin, J. Gregory Pauloski, Logan T. Ward, Valérie Hayot-Sasson, Murali Emani, Zhen Xie, Diangen Lin, Maulik Shukla, Ian T. Foster, James J. Davis, Michael E. Papka, Thomas S. Brettin, Prasanna Balaprakash, Gina Tourassi, John Gounley, Heidi A. Hanson, Thomas E. Potok, Massimiliano Lupo Pasini, Kate Evans, Dan Lu, Dalton D. Lunga, Junqi Yin, Sajal Dash, Feiyi Wang, Mallikarjun Shankar, Isaac Lyngaas, Xiao Wang, Guojing Cong, Pei Zhang, Ming Fan, Siyan Liu, Adolfy Hoisie, Shinjae Yoo, Yihui Ren, William Tang, Kyle Felker, Alexey Svyatkovskiy, Hang Liu, Ashwin M. Aji, Angela Dalton, Michael J. Schulte, Karl Schulz, Yuntian Deng, Weili Nie, Josh Romero, Christian Dallago, Arash Vahdat, Chaowei Xiao, Thomas Gibbs, Anima Anandkumar, Rick Stevens

CoRR, 2023

A deep learning approach for adaptive zoning.

Massimiliano Lupo Pasini, Luka Malenica, Kwitae Chong, Stuart R. Slattery

CoRR, 2023

DDStore: Distributed Data Store for Scalable Training of Graph Neural Networks on Large Atomistic Modeling Datasets.

Jong Youl Choi, Massimiliano Lupo Pasini, Pei Zhang, Kshitij Mehta, Frank Liu, Jonghyun Bae, Khaled Ibrahim

Proceedings of the SC '23 Workshops of The International Conference on High Performance Computing, 2023

2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems ^*.

Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi

Mach. Learn. Sci. Technol., 2022

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules.

Jong Youl Choi, Pei Zhang, Kshitij Mehta, Andrew E. Blanchard, Massimiliano Lupo Pasini

J. Cheminformatics, 2022

A deep learning approach to solve forward differential problems on graphs.

Yuanyuan Zhao, Massimiliano Lupo Pasini

CoRR, 2022

A deep learning approach for detection and localization of leaf anomalies.

Davide Calabrò, Massimiliano Lupo Pasini, Nicola Ferro, Simona Perotto

CoRR, 2022

Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems.

Massimiliano Lupo Pasini, Pei Zhang, Samuel Temple Reeve, Jong Youl Choi

CoRR, 2022

Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models.

Andrew E. Blanchard, Pei Zhang, Debsindhu Bhowmik, Kshitij Mehta, John Gounley, Samuel Temple Reeve, Stephan Irle, Massimiliano Lupo Pasini

Proceedings of the Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, 2022

Machine Learning for First Principles Calculations of Material Properties for Ferromagnetic Materials.

Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini, Junqi Yin

Proceedings of the Accelerating Science and Engineering Discoveries Through Integrated Research Infrastructure for Experiment, Big Data, Modeling and Simulation, 2022

2021

Scalable balanced training of conditional generative adversarial neural networks on image data.

Massimiliano Lupo Pasini, Vittorio Gabbi, Junqi Yin, Simona Perotto, Nouamane Laanait

J. Supercomput., 2021

A scalable algorithm for the optimization of neural network architectures.

Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach

Parallel Comput., 2021

Stable Anderson Acceleration for Deep Learning.

Massimiliano Lupo Pasini, Junqi Yin, Viktor Reshniak, Miroslav Stoyanov

CoRR, 2021

Fast and Accurate Predictions of Total Energy for Solid Solution Alloys with Graph Convolutional Neural Networks.

Massimiliano Lupo Pasini, Marko Burcul, Samuel Temple Reeve, Markus Eisenbach, Simona Perotto

Proceedings of the Driving Scientific and Engineering Discoveries Through the Integration of Experiment, Big Data, and Modeling and Simulation, 2021

Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks : *Full/Regular Research Paper submission for the symposium CSCI-ISAI: Artificial Intelligence.

Massimiliano Lupo Pasini, Junqi Yin

Proceedings of the International Conference on Computational Science and Computational Intelligence, 2021

2020

A parallel strategy for density functional theory computations on accelerated nodes.

Massimiliano Lupo Pasini, Bruno Turcksin, Wenjun Ge, Jean-Luc Fattebert

Parallel Comput., 2020

2019

Convergence analysis of Anderson-type acceleration of Richardson's iteration.

Massimiliano Lupo Pasini

Numer. Linear Algebra Appl., 2019

A greedy constructive algorithm for the optimization of neural network architectures.

Massimiliano Lupo Pasini, Junqi Yin, Ying Wai Li, Markus Eisenbach

CoRR, 2019

2017

Analysis of Monte Carlo accelerated iterative methods for sparse linear systems.

Michele Benzi, Thomas M. Evans, Steven P. Hamilton, Massimiliano Lupo Pasini, Stuart R. Slattery

Numer. Linear Algebra Appl., 2017