Advancing Fusion with Machine Learning Research Needs Workshop Report

Machine learning control for disruption and tearing mode avoidance

(2020) Yichen Fu et al.   PHYSICS OF PLASMAS

Capturing chemical intuition in synthesis of metal-organic frameworks

(2019) Seyed Mohamad Moosavi et al.   Nature Communications

Tripled yield in direct-drive laser fusion through statistical modelling

(2019) V. Gopalaswamy et al.   NATURE

Physics-constrained deep learning for high-dimensional surrogate modeling and uncertainty quantification without labeled data

(2019) Yinhao Zhu et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Data-driven discovery of PDEs in complex datasets

(2019) Jens Berg et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Predicting disruptive instabilities in controlled fusion plasmas through deep learning

(2019) Julian Kates-Harbeck et al.   NATURE

Machine learning for disruption warning on Alcator C-Mod, DIII-D, and EAST

(2019) Kevin Joseph Montes et al.   NUCLEAR FUSION

Three pitfalls to avoid in machine learning

(2019) Patrick Riley   NATURE

Making inertial confinement fusion models more predictive

(2019) Jim A. Gaffney et al.   PHYSICS OF PLASMAS

A Riccati solution for the ideal MHD plasma response with applications to real-time stability control

(2018) Alexander S. Glasser et al.   PHYSICS OF PLASMAS

Disruption prediction investigations using Machine Learning tools on DIII-D and Alcator C-Mod

(2018) C Rea et al.   PLASMA PHYSICS AND CONTROLLED FUSION

The genome of the offspring of a Neanderthal mother and a Denisovan father

(2018) Viviane Slon et al.   NATURE

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Variational Inference: A Review for Statisticians

(2017) David M. Blei et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION

Mastering the game of Go without human knowledge

(2017) David Silver et al.   NATURE

Helical core reconstruction of a DIII-D hybrid scenario tokamak discharge

(2017) M. Cianciosa et al.   NUCLEAR FUSION

Zonal flow generation in inertial confinement fusion implosions

(2017) J. L. Peterson et al.   PHYSICS OF PLASMAS

A reduced resistive wall mode kinetic stability model for disruption forecasting

(2017) J. W. Berkery et al.   PHYSICS OF PLASMAS

Structure Preserving Model Reduction of Parametric Hamiltonian Systems

(2017) Babak Maboudi Afkham et al.   SIAM JOURNAL ON SCIENTIFIC COMPUTING

Achievement of Sustained Net Plasma Heating in a Fusion Experiment with the Optometrist Algorithm

(2017) E. A. Baltz et al.   Scientific Reports

Image Super-Resolution Using Deep Convolutional Networks

(2016) Chao Dong et al.   IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE

Discovering governing equations from data by sparse identification of nonlinear dynamical systems

(2016) Steven L. Brunton et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Disruptions in ITER and strategies for their control and mitigation

(2015) M. Lehnen et al.   JOURNAL OF NUCLEAR MATERIALS

Human-level control through deep reinforcement learning

(2015) Volodymyr Mnih et al.   NATURE

Design and first applications of the ITER integrated modelling & analysis suite

(2015) F. Imbeaux et al.   NUCLEAR FUSION

Three-dimensional equilibrium reconstruction on the DIII-D device

(2015) S.A. Lazerson et al.   NUCLEAR FUSION

Novel aspects of plasma control in ITER

(2015) D. Humphreys et al.   PHYSICS OF PLASMAS

On generalized moving least squares and diffuse derivatives

(2011) D. Mirzaei et al.   IMA JOURNAL OF NUMERICAL ANALYSIS

Computer Model Calibration Using High-Dimensional Output

(2008) Dave Higdon et al.   JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION