Massimiliano Lupo Pasini
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Analysis of Monte Carlo accelerated iterative methods for sparse linear systems
M Benzi, TM Evans, SP Hamilton, M Lupo Pasini, SR Slattery
Numerical Linear Algebra with Applications 24 (3), e2088, 2017
Convergence analysis of Anderson‐type acceleration of Richardson's iteration
M Lupo Pasini
Numerical Linear Algebra with Applications 26 (4), e2241, 2019
Fast and stable deep-learning predictions of material properties for solid solution alloys
M Lupo Pasini, YW Li, J Yin, J Zhang, K Barros, M Eisenbach
Journal of Physics: Condensed Matter 33 (8), 084005, 2020
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
M Lupo Pasini, P Zhang, ST Reeve, JY Choi
Machine Learning: Science and Technology 3 (2), 025007, 2022
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
JY Choi, P Zhang, K Mehta, A Blanchard, M Lupo Pasini
Journal of Cheminformatics 14 (70), https://trebuchet.public.springernature., 2022
A scalable algorithm for the optimization of neural network architectures
M Lupo Pasini, J Yin, YW Li, M Eisenbach
Parallel Computing 104, 102788, 2021
Hierarchical model reduction driven by a proper orthogonal decomposition for parametrized advection-diffusion-reaction problems
M Lupo Pasini, S Perotto
Electronic transactions on numerical analysis ETNA 55, 187-212, 2022
M Lupo Pasini, ST Reeve, P Zhang, JY Choi 20211019, 2021
Graph neural networks predict energetic and mechanical properties for models of solid solution metal alloy phases
M Lupo Pasini, GS Jung, S Irle
Computational Materials Science 224, 112141, 2023
Anderson Acceleration for Distributed Training of Deep Learning Models
M Lupo Pasini, J Yin, V Reshniak, M Stoyanov
SoutheastCon 2022, 289-295, 2022
Fast and accurate predictions of total energy for solid solution alloys with graph convolutional neural networks
M Lupo Pasini, M Burc̆ul, ST Reeve, M Eisenbach, S Perotto
Smoky Mountains Computational Sciences and Engineering Conference, 79-98, 2021
Stable Anderson Acceleration for Deep Learning
M Lupo Pasini, J Yin, V Reshniak, M Stoyanov
arXiv e-prints, arXiv: 2110.14813, 2021
A parallel strategy for density functional theory computations on accelerated nodes
M Lupo Pasini, B Turcksin, W Ge, JL Fattebert
Parallel Computing 100, 102703, 2020
Computational Workflow for Accelerated Molecular Design Using Quantum Chemical Simulations and Deep Learning Models
AE Blanchard, P Zhang, D Bhowmik, K Mehta, J Gounley, ST Reeve, ...
Accelerating Science and Engineering Discoveries Through Integrated Research …, 2023
Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data
M Lupo Pasini, S Perotto
Journal of Scientific Computing 94 (36), 1-22, 2023
Scalable balanced training of conditional generative adversarial neural networks on image data
M Lupo Pasini, V Gabbi, J Yin, Perotto, Simona, N Laanait
Journal of Supercomputing 77 (11), 13358-13384, 2021
HI-POD: hierarchical model reduction driven by a proper orthogonal decomposition for advection diffusion reaction problems
Politecnico di Milano, 2012
Gdb-9-ex: Quantum chemical prediction of UV/VIS absorption spectra for gdb-9 molecules
M Lupo Pasini, P Yoo, K Mehta, S Irle
Oak Ridge National Lab.(ORNL), Oak Ridge, TN (United States). Oak Ridge …, 2022
A Neural Network Approach to Predict Gibbs Free Energy of Ternary Solid Solutions
P Laiu, Y Yang, M Lupo Pasini, JY Choi, D Shin
Journal of Phase Equilibria and Diffusion 43, 916-930, 2022
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks
M Lupo Pasini, J Yin
Journal of Supercomputing, 2022
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