61 citations · 108 across the 6 of their papers we have counts for
7 papers
Metaheuristic conditional neural network for harvesting skyrmionic metastable states
Qichen Xu, I. P. Miranda, Manuel Pereiro +8
We present a metaheuristic conditional neural-network-based method aimed at identifying physically interesting metastable states in a potential energy surface of high rugosity. To…
Genetic-tunneling driven energy optimizer for spin systems
Qichen Xu, Zhuanglin Shen, Manuel Pereiro +3
A long-standing and difficult problem in, e.g., condensed matter physics is how to find the ground state of a complex many-body system where the potential energy surface has a larg…
A Long Short-term Memory Based Recurrent Neural Network for Interventional MRI Reconstruction
Ruiyang Zhao, Zhao He, Tao Wang +8
Interventional magnetic resonance imaging (i-MRI) for surgical guidance could help visualize the interventional process such as deep brain stimulation (DBS), improving the surgery…
StreamBrain: An HPC Framework for Brain-like Neural Networks on CPUs, GPUs and FPGAs
Artur Podobas, Martin Svedin, Steven W. D. Chien +5
The modern deep learning method based on backpropagation has surged in popularity and has been used in multiple domains and application areas. At the same time, there are other --…
Automatic Particle Trajectory Classification in Plasma Simulations
Stefano Markidis, Ivy Peng, Artur Podobas +3
Numerical simulations of plasma flows are crucial for advancing our understanding of microscopic processes that drive the global plasma dynamics in fusion devices, space, and astro…
Automated classification of plasma regions using 3D particle energy distributions
Vyacheslav Olshevsky, Yuri V. Khotyaintsev, Ahmad Lalti +8
We investigate the properties of the ion sky maps produced by the Dual Ion Spectrometers (DIS) from the Fast Plasma Investigation (FPI). We have trained a convolutional neural netw…
Characterizing Deep-Learning I/O Workloads in TensorFlow
Steven W. D. Chien, Stefano Markidis, Chaitanya Prasad Sishtla +4
The performance of Deep-Learning (DL) computing frameworks rely on the performance of data ingestion and checkpointing. In fact, during the training, a considerable high number of…