Applications of machine learning in real-time control systems: a review
X Zhao, Y Sun, Y Li, N Jia, J Xu - Measurement Science and …, 2024 - iopscience.iop.org
Real-time control systems (RTCS) have become an indispensable part of modern industry,
finding widespread applications in fields such as robotics, intelligent manufacturing and …
finding widespread applications in fields such as robotics, intelligent manufacturing and …
Solving real-world optimization tasks using physics-informed neural computing
J Seo - Scientific Reports, 2024 - nature.com
Optimization tasks are essential in modern engineering fields such as chip design,
spacecraft trajectory determination, and reactor scenario development. Recently, machine …
spacecraft trajectory determination, and reactor scenario development. Recently, machine …
Reinforcement learning for sustainable energy: A survey
The transition to sustainable energy is a key challenge of our time, requiring modifications in
the entire pipeline of energy production, storage, transmission, and consumption. At every …
the entire pipeline of energy production, storage, transmission, and consumption. At every …
Intelligent hydrogen-ammonia combined energy storage system with deep reinforcement learning
P Lan, S Chen, Q Li, K Li, F Wang, Y Zhao - Renewable Energy, 2024 - Elsevier
To achieve carbon neutrality, hydrogen and ammonia are considered promising energy
carriers for renewable energy. Efficient use of these resources has become a critical …
carriers for renewable energy. Efficient use of these resources has become a critical …
Past rewinding of fluid dynamics from noisy observation via physics-informed neural computing
J Seo - Physical Review E, 2024 - APS
Reconstructing the past of observed fluids has been known as an ill-posed problem due to
both numerical and physical challenges, especially when observations are distorted by …
both numerical and physical challenges, especially when observations are distorted by …
Surrogate model of turbulent transport in fusion plasmas using machine learning
H Li, L Wang, YL Fu, ZX Wang, TB Wang, JQ Li - Nuclear Fusion, 2024 - iopscience.iop.org
The advent of machine learning (ML) has revolutionized the research of plasma
confinement, offering new avenues for exploration. It enables the construction of models that …
confinement, offering new avenues for exploration. It enables the construction of models that …
Highest fusion performance without harmful edge energy bursts in tokamak
The path of tokamak fusion and International thermonuclear experimental reactor (ITER) is
maintaining high-performance plasma to produce sufficient fusion power. This effort is …
maintaining high-performance plasma to produce sufficient fusion power. This effort is …
Robust Technology Regulation
We analyze how uncertain technologies should be robustly regulated. An agent develops a
new technology and, while privately learning about its harms and benefits, continually …
new technology and, while privately learning about its harms and benefits, continually …
Adapted Swin Transformer-based real-time plasma shape detection and control in HL-3
Q Dong, Z Chen, R Li, Z Yang, F Gao, Y Chen… - Nuclear …, 2025 - iopscience.iop.org
In the field of magnetic confinement plasma control, the accurate feedback of plasma
position and shape primarily relies on calculations derived from magnetic measurements …
position and shape primarily relies on calculations derived from magnetic measurements …
The application of artificial intelligence-assisted technology in cultural and creative product design
J Liang - Scientific Reports, 2024 - nature.com
This study proposes a novel artificial intelligence (AI)-assisted design model that combines
Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and …
Variational Autoencoders (VAE) with reinforcement learning (RL) to enhance innovation and …