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Found 12 papers, 2 papers with code
Scalable Training of Trustworthy and Energy-Efficient Predictive Graph Foundation Models for Atomistic Materials Modeling: A Case Study with HydraGNN
The HydraGNN architecture enables the GFM to achieve near-linear strong scaling performance using more than 2, 000 GPUs on Perlmutter and 16, 000 GPUs on Frontier.
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences.
A deep learning approach for adaptive zoning
We also show that the surrogate DL model reduces the computational time to perform adaptive zoning by at least a 2x factor with respect to standard techniques without compromising the accuracy of the reconstruction of the physical quantities of interest.
A deep learning approach for detection and localization of leaf anomalies
The detection and localization of possible diseases in crops are usually automated by resorting to supervised deep learning approaches.
A deep learning approach to solve forward differential problems on graphs
We propose a novel deep learning (DL) approach to solve one-dimensional non-linear elliptic, parabolic, and hyperbolic problems on graphs.
Stable Parallel Training of Wasserstein Conditional Generative Adversarial Neural Networks
We propose a stable, parallel approach to train Wasserstein Conditional Generative Adversarial Neural Networks (W-CGANs) under the constraint of a fixed computational budget.
Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules
Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures.
Hierarchical model reduction driven by machine learning for parametric advection-diffusion-reaction problems in the presence of noisy data
We propose a new approach to generate a reliable reduced model for a parametric elliptic problem, in the presence of noisy data.
Multi-task graph neural networks for simultaneous prediction of global and atomic properties in ferromagnetic systems
We train HydraGNN on an open-source ab initio density functional theory (DFT) dataset for iron-platinum (FePt) with a fixed body centered tetragonal (BCT) lattice structure and fixed volume to simultaneously predict the mixing enthalpy (a global feature of the system), the atomic charge transfer, and the atomic magnetic moment across configurations that span the entire compositional range.
Stable Anderson Acceleration for Deep Learning
Anderson acceleration (AA) is an extrapolation technique designed to speed-up fixed-point iterations like those arising from the iterative training of DL models.
Scalable Balanced Training of Conditional Generative Adversarial Neural Networks on Image Data
We propose a distributed approach to train deep convolutional generative adversarial neural network (DC-CGANs) models.
A scalable constructive algorithm for the optimization of neural network architectures
We propose a new scalable method to optimize the architecture of an artificial neural network.
