In magnetically confined fusion device, the complex, multiscale, and nonlinear dynamics of plasmas necessitate the integration of extensive diagnostic systems to effectively monitor and control plasma behaviour. The complexity and uncertainty arising from these extensive systems and their tangled interrelations has long posed a significant obstacle to the acceleration of fusion energy development. In this work, a large-scale model, fusion masked auto-encoder (FusionMAE) is pre-trained to compress the information from 88 diagnostic signals into a concrete embedding, to provide a unified interface between diagnostic systems and control actuators. Two mechanisms are proposed to ensure a meaningful embedding: compression-reduction and missing-signal reconstruction. Upon completion of pre-training, the model acquires the capability for 'virtual backup diagnosis', enabling the inference of missing diagnostic data with 96.7% reliability. Furthermore, the model demonstrates three emergent capabilities: automatic data analysis, universal control-diagnosis interface, and enhancement of control performance on multiple tasks. This work pioneers large-scale AI model integration in fusion energy, demonstrating how pre-trained embeddings can simplify the system interface, reducing necessary diagnostic systems and optimize operation performance for future fusion reactors.

Proton-boron (p-11B) fusion presents a promising path towards sustainable, neutron-free energy generation. However, its implementation is hindered by extreme operational conditions, such as plasma temperatures exceeding billions of degrees and the complexity of controlling high-energy particles. Traditional control systems face significant challenges in managing the highly dynamic and non-linear behavior of the plasma. In this paper, we propose a novel approach utilizing backpropagation-based neural networks to autonomously control key parameters in a proton-boron fusion reactor. Our method leverages real-time feedback and learning from physical data to adapt to changing plasma conditions, offering a potential breakthrough in stable and efficient p-11B fusion. Furthermore, we expand on the scalability and generalization of our approach to other fusion systems and future AI technologies.

Fusion, which promises practically unlimited, carbon-free energy using the same processes that power the sun, is at the heart of a worldwide research effort that could help mitigate climate change. A multidisciplinary team of researchers is now bringing tools and insights from machine learning to aid this effort. Scientists from MIT and elsewhere have used computer-vision models to identify and track turbulent structures that appear under the conditions needed to facilitate fusion reactions. Monitoring the formation and movements of these structures, called filaments or "blobs," is important for understanding the heat and particle flows exiting from the reacting fuel, which ultimately determines the engineering requirements for the reactor walls to meet those flows. However, scientists typically study blobs using averaging techniques, which trade details of individual structures in favor of aggregate statistics.

The most powerful supercomputers on the planet are used to perform all manner of complex operations. Increasingly, they are used to enable artificial intelligence for research that could one day impact billions of people. The world's fastest and most powerful high-performance computing (HPC) supercomputers are front and center at the International Supercomputing Conference (ISC) which runs from May 29 to June 2 in Hamburg, Germany. As part of the ISC event, Nvidia will provide insight about its latest HPC systems and the use cases they enable. "HPC plus AI is really the transformational tool of scientific computing," Dion Harris, lead technical product marketing manager for accelerated computing, said in a media briefing ahead of ISC.

Edge plasma turbulence is critical to the performance of magnetic confinement fusion devices. Towards better understanding edge turbulence in both theory and experiment, a custom-built physics-informed deep learning framework constrained by partial differential equations is developed to accurately learn turbulent fields consistent with the two-fluid theory from partial observations of electron pressure. This calculation is not otherwise possible using conventional equilibrium models. With this technique, the first direct quantitative comparisons of turbulent fields between electrostatic two-fluid theory and electromagnetic gyrokinetic modelling are demonstrated with good overall agreement found in magnetized helical plasmas at low normalized pressure. To translate these computational techniques to experimental fusion plasmas, a novel method to translate brightness measurements of HeI line radiation into local plasma fluctuations is demonstrated via a newly created deep learning framework that integrates neutral transport physics and collisional radiative theory for the $3^3 D - 2^3 P$ transition in atomic helium. Using fast camera data on the Alcator C-Mod tokamak, this thesis presents the first 2-dimensional time-dependent experimental measurements of the turbulent electron density, electron temperature, and neutral density in a fusion plasma using a single spectral line. With this experimentally inferred data, initial estimates of the 2-dimensional turbulent electric field consistent with drift-reduced Braginskii theory under the framework of an axisymmetric fusion plasma with purely toroidal field are calculated. The inclusion of atomic helium effects on particle and energy sources are found to strengthen correlations between the electric field and electron pressure while broadening turbulent field amplitudes which impact ${\bf E \times B}$ flows and shearing rates.

The green energy revolution promised by nuclear fusion is now a step closer, thanks to the first successful use of a cutting-edge artificial intelligence system to shape the superheated hydrogen plasmas inside a fusion reactor. The successful trial indicates that the use of AI could be a breakthrough in the long-running search for electricity generated from nuclear fusion -- bringing its introduction to replace fossil fuels and nuclear fission on modern power grids tantalizingly closer. "I think AI will play a very big role in the future control of tokamaks and in fusion science in general," Federico Felici, a physicist at the Swiss Federal Institute of Technology in Lausanne (EPFL) and one of the leaders on the project, told Live Science. "There's a huge potential to unleash AI to get better control and to figure out how to operate such devices in a more effective way." Felici is a lead author of a new study describing the project published in the journal Nature.

At the beginning of everything, there was energy. Energy is one of the most important things in the universe . Without usable energy, you could not read this article. Fusion power gives life to earth. Sun is a very big fusion reactor, without it earth would be frozen.

Controlling nuclear fusion on Earth is hard, however. The problem is that atomic nuclei repel each other. Smashing them together inside a reactor can only be done at extremely high temperatures, often reaching hundreds of millions of degrees--hotter than the center of the sun. At these temperatures, matter is neither solid, liquid, nor gas. It enters a fourth state, known as plasma: a roiling, superheated soup of particles.

DeepMind, the AI lab backed by Google parent company Alphabet, today announced that it used AI to successfully control superheated matter inside a nuclear fusion reactor. The lab claims that the system, which is detailed in a paper published in the journal Nature, could allow scientists to investigate how such matter reacts under different conditions. While DeepMind remains engaged in prestige projects like systems that can beat champions at StarCraft II and Go, the lab has in recent years turned its attention to more practical domains, such as code generation, language processing, weather forecasting, app recommendations, and video compression. DeepMind licenses many of its innovations to other Alphabet-owned businesses, like autonomous car company Waymo and YouTube, and it recently launched a spinoff outfit -- Isomorphic Labs -- focused on drug discovery. "While there is still much work to be done … we are pleased that the results indicate the power of AI to accelerate and assist fusion science, most likely augmenting human expertise in the field and serving as a tool to discover new and creative approaches for [fusion reactor control] and beyond," Martin Riedmiller, a research scientist at DeepMind, said during a press briefing this week.

In nuclear fusion, the atomic nuclei of hydrogen atoms get forced together to form heavier atoms, like helium. This produces a lot of energy relative to a tiny amount of fuel, making it a very efficient source of power. It is far cleaner and safer than fossil fuels or conventional nuclear power, which is created by fission--forcing nuclei apart. It is also the process that powers stars. Controlling nuclear fusion on Earth is hard, however.