This work presents predictive hydrodynamic simulations empowered by artificial intelligence (AI) for laser driven implosion experiments, taking the double-cone ignition (DCI) scheme as an example. A Transformer-based deep learning model MULTI-Net is established to predict implosion features according to laser waveforms and target radius. A Physics-Informed Decoder (PID) is proposed for high-dimensional sampling, significantly reducing the prediction errors compared to Latin hypercube sampling. Applied to DCI experiments conducted on the SG-II Upgrade facility, the MULTI-Net model is able to predict the implosion dynamics measured by the x-ray streak camera. It is found that an effective laser absorption factor about 65\% is suitable for the one-dimensional simulations of the DCI-R10 experiments. For shot 33, the mean implosion velocity and collided plasma density reached 195 km/s and 117 g/cc, respectively. This study demonstrates a data-driven AI framework that enhances the prediction ability of simulations for complicated laser fusion experiments.

Visualized are two-dimensional pressure fluctuations within a larger three-dimensional magnetically confined fusion plasma simulation. With recent advances in machine-learning techniques, these types of partial observations provide new ways to test reduced turbulence models in both theory and experiment. MIT researchers are testing a simplified turbulence theory's ability to model complex plasma phenomena using a novel machine-learning technique. To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel.

To make fusion energy a viable resource for the world's energy grid, researchers need to understand the turbulent motion of plasmas: a mix of ions and electrons swirling around in reactor vessels. The plasma particles, following magnetic field lines in toroidal chambers known as tokamaks, must be confined long enough for fusion devices to produce significant gains in net energy, a challenge when the hot edge of the plasma (over 1 million degrees Celsius) is just centimeters away from the much cooler solid walls of the vessel. Abhilash Mathews, a PhD candidate in the Department of Nuclear Science and Engineering working at MIT's Plasma Science and Fusion Center (PSFC), believes this plasma edge to be a particularly rich source of unanswered questions. A turbulent boundary, it is central to understanding plasma confinement, fueling, and the potentially damaging heat fluxes that can strike material surfaces – factors that impact fusion reactor designs. To better understand edge conditions, scientists focus on modeling turbulence at this boundary using numerical simulations that will help predict the plasma's behavior.

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy(link is external) that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U) -- the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma(link is external) that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas -- largely self-heating fusion reactions -- that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper(link is external) in Nuclear Fusion that describes the machine learning tactics.

Machine learning, a technique used in the artificial intelligence (AI) software behind self-driving cars and digital assistants, now enables scientists to address key challenges to harvesting on Earth the fusion energy that powers the sun and stars. The technique recently empowered physicist Dan Boyer of the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) to develop fast and accurate predictions for advancing control of experiments in the National Spherical Torus Experiment-Upgrade (NSTX-U)--the flagship fusion facility at PPPL that is currently under repair. Such AI predictions could improve the ability of NSTX-U scientists to optimize the components of experiments that heat and shape the magnetically confined plasma that fuels fusion experiments. By optimizing the heating and shaping of the plasma scientists will be able to more effectively study key aspects of the development of burning plasmas--largely self-heating fusion reactions--that will be critical for ITER, the international experiment under construction in France, and future fusion reactors. "This is a step toward what we should do to optimize the actuators," said Boyer, author of a paper in Nuclear Fusion that describes the machine learning tactics.

In Marvel's comic universe following the end of World War II Howard Stark tries to tap into the energy of the mystical "Tesseract" and develops the arc reactor -- a technology he believes to hold the key to unlimited, sustainable energy and would make nuclear energy look like an AAA battery. However, the perfect reactor cannot be built without a certain theoretical element and he lacks the technology to synthesize it. In the film "Iron Man", his son Tony Stark builds a miniature version of the Arc Reactor when held hostage in an Afghan cave to power an electromagnet, which keeps deadly shrapnel from piercing his heart. Even this small reactor has a remarkable output of 3 GJ/s -- as much as three times the average energy produced by a nuclear power plant. As the reactor's waste products threaten to poison him, Tony searches for new elements for the reaction.