Publications (12)
Training Distributed Deep Recurrent Neural Networks with Mixed Precision on GPU Clusters
Alexey Svyatkovskiy, Julian Kates-Harbeck, William Tang
In this paper, we evaluate training of deep recurrent neural networks with half-precision floats. We implement a distributed, data-parallel, synchronous training algorithm by integ…
Applications and Techniques for Fast Machine Learning in Science
Allison McCarn Deiana, Nhan Tran, Joshua Agar +84
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time…
Reconstruction of tokamak plasma safety factor profile using deep learning
Xishuo Wei, Ge Dong, Shuying Sun +3
In tokamak operations, accurate equilibrium reconstruction is essential for reliable real-time control and realistic post-shot instability analysis. The safety factor (q) profile d…
Implementation of AI/Deep Learning Disruption Predictor into a Plasma Control System
William Tang, Ge Dong, Jayson Barr +17
This paper reports on advances to the state-of-the-art deep-learning disruption prediction models based on the Fusion Recurrent Neural Network (FRNN) originally introduced a 2019 N…
Modern Gyrokinetic Particle-In-Cell Simulation of Fusion Plasmas on Top Supercomputers
Bei Wang, Stephane Ethier, William Tang +4
The Gyrokinetic Toroidal Code at Princeton (GTC-P) is a highly scalable and portable particle-in-cell (PIC) code. It solves the 5D Vlasov-Poisson equation featuring efficient utili…
Deep learning based surrogate model for first-principles global simulations of fusion plasmas
Ge Dong, Xishuo Wei, Jian Bao +3
The accurate identification and control of plasma instabilities is important for successful fusion experiments. First-principles simulations which can provide physics based instabi…
DA-SegFormer: Damage-Aware Semantic Segmentation for Fine-Grained Disaster Assessment
Kevin Zhu, William Tang, Raphael Hay Tene +3
Rapid and accurate damage assessment following natural disasters is critical for effective emergency response. However, identifying fine-grained damage levels (e.g., distinguishing…
Deep machine learning-assisted multiphoton microscopy to reduce light exposure and expedite imaging
Stephen McAleer, Alex Fast, Yuntian Xue +5
Two-photon excitation fluorescence (2PEF) allows imaging of tissue up to about one millimeter in thickness. Typically, reducing fluorescence excitation exposure reduces the quality…
AI-Machine Learning-Enabled Tokamak Digital Twin
William Tang, Eliot Feibush, Ge Dong +9
In addressing the Department of Energy's April, 2022 announcement of a Bold Decadal Vision for delivering a Fusion Pilot Plant by 2035, associated software tools need to be develop…
Fully Convolutional Spatio-Temporal Models for Representation Learning in Plasma Science
Ge Dong, Kyle Gerard Felker, Alexey Svyatkovskiy +2
We have trained a fully convolutional spatio-temporal model for fast and accurate representation learning in the challenging exemplar application area of fusion energy plasma scien…
FTL: Transfer Learning Nonlinear Plasma Dynamic Transitions in Low Dimensional Embeddings via Deep Neural Networks
Zhe Bai, Xishuo Wei, William Tang +3
Deep learning algorithms provide a new paradigm to study high-dimensional dynamical behaviors, such as those in fusion plasma systems. Development of novel model reduction methods,…
DeepSpeed4Science Initiative: Enabling Large-Scale Scientific Discovery through Sophisticated AI System Technologies
Shuaiwen Leon Song, Bonnie Kruft, Minjia Zhang +89
In the upcoming decade, deep learning may revolutionize the natural sciences, enhancing our capacity to model and predict natural occurrences. This could herald a new era of scient…