Introduction: The Power of Stars, Within Reach

Somewhere deep inside the core of our sun, hydrogen atoms are being crushed together under unimaginable pressure and heat, fusing into helium and releasing the energy that lights our world. For more than seventy years, scientists have been trying to replicate that process here on Earth — not with gravity, as the sun does, but with powerful magnetic fields and temperatures exceeding 100 million degrees Celsius. The promise is staggering: a single glass of seawater contains enough hydrogen fuel to produce the energy equivalent of burning 300 litres of gasoline, with no carbon emissions, no long-lived radioactive waste, and no risk of meltdown.

Yet fusion has remained stubbornly out of reach. The old joke — that fusion energy is always thirty years away — has haunted the field for decades. But something has changed in the last few years. A new and powerful ally has entered the picture: artificial intelligence. Specifically, researchers are building what are called AI digital twins — virtual replicas of fusion reactors that can predict, in real time, what the superheated plasma inside will do next and adjust the machine’s controls before anything goes wrong. These systems represent a marriage of cutting-edge machine learning with the most extreme physics on Earth, and they are delivering results that would have seemed like science fiction just a decade ago.

This is the story of how that technology began, how it works, who the key players are, what records have been shattered, where the failures and honest limitations lie, and what the future holds for the quest to bring the power of the stars down to Earth.

Part One: What Is Nuclear Fusion, and Why Is It So Hard?

Nuclear fusion is the process that powers every star in the universe. When two light atomic nuclei — typically isotopes of hydrogen called deuterium and tritium — are forced close enough together, they overcome their natural electromagnetic repulsion and fuse into a single, heavier nucleus (helium), releasing an enormous burst of energy in the process. A single fusion reaction releases roughly four million times more energy per kilogram of fuel than burning coal.

The catch is that achieving fusion requires conditions found nowhere naturally on our planet. The fuel must be heated to at least 100 million degrees Celsius — roughly six times hotter than the centre of the sun — and held together long enough for enough reactions to occur. At these temperatures, matter exists as plasma, an electrically charged gas that behaves like a writhing, turbulent storm. Containing this plasma is the central engineering challenge of fusion energy.

The Tokamak: Trapping Lightning in a Bottle

The most advanced approach to magnetic confinement fusion uses a device called a tokamak — a Russian acronym for “toroidal chamber with magnetic coils.” Invented in the 1950s by Soviet physicists Andrei Sakharov and Igor Tamm, a tokamak uses powerful magnets to create a doughnut-shaped magnetic cage that holds the plasma away from the physical walls of the reactor. If the plasma touches the walls, it instantly cools and the reaction dies — or worse, it damages the machine.

But plasma is wildly unstable. It writhes, kinks, and tears in ways that are fiendishly difficult to predict. Scientists categorise these disruptions by name: Edge Localised Modes (ELMs) are periodic bursts that eject heat and particles from the plasma edge. Tearing modes are spiral instabilities in the magnetic field that can rip the plasma apart. Disruptions are sudden, catastrophic losses of confinement that can slam the full energy of the plasma into the reactor walls in milliseconds. At the scale of a full power plant, a single unmitigated disruption could generate electromagnetic forces comparable to the weight of a wide-body passenger aircraft.

Controlling all of this requires adjusting magnetic coil currents, heating power, and fuel injection on timescales of microseconds to milliseconds — far faster than any human operator can manage. This is where AI enters the story.

Part Two: What Is an AI Digital Twin?

The concept of a “digital twin” did not originate in fusion. The idea of a virtual replica linked to a physical system was formally articulated by Michael Grieves at a manufacturing engineering conference in 2002, and later popularised by NASA, which used digital twins to monitor spacecraft systems during flight. The term refers to a computational model that mirrors a real-world object or process in real time, continuously ingesting sensor data, updating its internal state, and — crucially — feeding predictions and control commands back to the physical system.

In the context of fusion, a digital twin is a virtual replica of the entire reactor and its plasma. Think of it as a parallel universe running inside a supercomputer, where the AI can test thousands of “what if” scenarios in the time it takes the real plasma to evolve through a single heartbeat. The architecture typically has four layers:

The Physics Core: At the foundation sits a physics-based simulation solving the equations of magnetohydrodynamics (MHD) — the mathematical framework governing how electrically conducting fluids behave in magnetic fields. These equations describe plasma equilibrium, stability, and transport.

The Machine Learning Layer: Full physics simulations are far too slow for real-time use. A single gyrokinetic turbulence simulation can take millions of CPU-hours. So researchers train neural networks to approximate these physics codes, achieving speedups of up to a million times while maintaining accuracy. These are called surrogate models.

The Data Assimilation Interface: Real-time measurements from dozens or hundreds of sensors — magnetic probes, interferometers, Thomson scattering lasers for temperature and density — flow continuously into the digital twin, keeping its virtual plasma synchronised with reality.

The Control Interface: Based on its predictions, the digital twin sends commands to the reactor’s actuators: magnetic coil currents for plasma shape and position, neutral beam injectors and microwave heaters for temperature control, and gas puffers for density management.

When all four layers work together in a closed feedback loop — sensing, predicting, deciding, acting, and learning — you have a true digital twin. It is, in essence, a thinking machine that can feel the plasma’s pulse and respond before trouble arrives.

Part Three: How It All Began

The Early Pioneers (1994–2018)

AI in fusion is not as new as headlines suggest. Neural networks were first applied to plasma disruption prediction as early as 1994, when researchers used data from Princeton’s Tokamak Fusion Test Reactor (TFTR) to train early classifiers. Through the 2000s, teams at JET (the world’s largest tokamak, in Oxfordshire, England) and ASDEX Upgrade (in Garching, Germany) refined neural network disruption predictors. By 2003–2005, C.G. Windsor at Culham demonstrated that a predictor trained on one tokamak could partially transfer to another — an early hint of cross-machine learning.

The deep learning revolution hit fusion in April 2019, when Princeton physicists Julian Kates-Harbeck, Alexey Svyatkovskiy, and William Tang published the Fusion Recurrent Neural Network (FRNN) in Nature. Using Long Short-Term Memory networks, they achieved the first cross-machine disruption prediction — training on data from the DIII-D tokamak in San Diego and successfully predicting disruptions on JET in England. It was a landmark: proof that AI could learn fundamental plasma physics, not just memorise the quirks of a single machine.

DeepMind Enters the Arena (2022)

The moment that electrified both the AI and fusion communities came in February 2022, when Google’s DeepMind published a paper in Nature describing a deep reinforcement learning system that controlled plasma inside the TCV tokamak at the Swiss Plasma Centre (EPFL) in Lausanne, Switzerland. The collaboration had begun at a 2018 London hackathon between DeepMind researchers — including Jonas Degrave, Jonas Buchli, and Martin Riedmiller — and EPFL plasma physicist Federico Felici.

Their AI took 90 simultaneous measurements (34 magnetic flux loops, 38 field probes, and 19 coil currents) as input and output voltage commands to all 19 magnetic coils at a rate of 10,000 updates per second — one command every 100 microseconds. Trained entirely in simulation and then deployed “zero-shot” on the real tokamak (meaning with no additional real-world training), the single neural network replaced the entire conventional multi-component control system.

The results were remarkable. The AI produced elongated plasma shapes, negative-triangularity configurations (which reduce turbulence), and exotic “snowflake” divertor shapes — all standard requests. But then it did something unprecedented: it simultaneously sustained two completely independent plasma “droplets” inside the same tokamak. No human-designed control system had ever achieved this. It was a proof of concept that an AI could discover novel plasma configurations that human engineers had never imagined.

Part Four: Real-World Breakthroughs and Record-Breaking Results

Princeton’s AI Prevents Plasma Tearing (2024)

In February 2024, a team led by Jaemin Seo, SangKyeun Kim, and Egemen Kolemen at Princeton published in Nature a deep reinforcement learning system deployed on the DIII-D tokamak at General Atomics in San Diego. Their AI predicted the onset of dangerous tearing instabilities a full 300 milliseconds in advance and proactively adjusted neutral beam heating power and magnetic field parameters to prevent them entirely. Three hundred milliseconds may not sound like much, but in the life of a plasma that can tear itself apart in less than a thousandth of a second, it is an eternity. The system maintained high-performance H-mode operation under conditions relevant to the future ITER reactor — a direct step toward commercial fusion.

Japan’s First True Digital Twin Plasma Control (2024)

In January 2024, a team led by Yuya Morishita at Kyoto University and Japan’s National Institute for Fusion Science published in Scientific Reports the first claimed demonstration of predictive plasma control using a genuine digital twin. Working on the Large Helical Device (LHD) stellarator in Toki, Japan, they deployed the ASTI (Integrated Simulation for Toroidal Plasma) system — combining physics-based transport models with real-time data assimilation. The system ran simulations in parallel with actual experiments, predicted an impending density collapse, and adjusted electron cyclotron heating in real time to prevent it. This remains the closest implementation to the strict engineering definition of a digital twin in fusion: bidirectional data flow, dynamic updating, and a closed feedback loop.

NIF’s Ignition: AI Helped Predict the Historic Shot

On December 5, 2022, the National Ignition Facility (NIF) at Lawrence Livermore National Laboratory in California achieved what many had thought impossible: the first-ever demonstration of fusion ignition in a laboratory. Using 192 of the world’s most powerful lasers to compress a tiny capsule of deuterium-tritium fuel, NIF produced 3.15 megajoules of fusion energy from 2.05 megajoules of laser energy delivered to the target — a fusion gain (Q) of approximately 1.54. It was the first time in history that a fusion experiment produced more energy from fusion reactions than was put into the fuel.

AI played a meaningful supporting role. LLNL’s Cognitive Simulation (CogSim) framework — deep neural networks trained on more than 150,000 hydrodynamic simulations, refined through transfer learning on decades of experimental data — had predicted a greater than 70 percent probability of ignition for that specific shot configuration. CogSim reduced prediction errors from roughly 110 percent to less than 7 percent and is now integrated into NIF’s standard workflow. By October 2025, NIF had achieved at least 10 total ignition shots, with a peak result of Q ≈ 4.13 (8.6 megajoules of fusion output from 2.08 megajoules of laser input).

Important caveat: NIF uses inertial confinement fusion (lasers), not magnetic confinement (tokamaks). Its Q values measure energy delivered to the fuel capsule, not the total energy consumed by the laser system, which is vastly larger. NIF is primarily a nuclear weapons science facility, not a power plant prototype. These results are transformative for plasma physics but do not directly translate to commercial electricity generation.

JET’s Final Records: A Farewell to the King

The Joint European Torus (JET) at Culham, England — the world’s largest operational tokamak for decades — set its final fusion energy records before shutting down in December 2023. In its December 2021 deuterium-tritium campaign, JET produced 59 megajoules of fusion energy over approximately 5 seconds. In its final campaign in October 2023, it surpassed even that, achieving 69.26 megajoules over roughly 6 seconds. These remain the all-time records for sustained magnetic confinement fusion energy output. However, JET’s plasma Q was approximately 0.33 — meaning it produced about one-third as much fusion energy as was injected to heat the plasma. Critically, AI and digital twin systems were not central drivers of these achievements; they relied on decades of conventional physics-based optimisation and the ITER-Like Wall installed in 2011.

KSTAR: Korea’s Artificial Sun

South Korea’s KSTAR (Korea Superconducting Tokamak Advanced Research) set a different kind of record during its December 2023 to February 2024 campaign: sustaining plasma at 100 million degrees Celsius for 48 seconds, the longest duration at fusion-relevant temperatures for any tokamak. This achievement was primarily attributed to hardware upgrades — specifically, the replacement of carbon-fibre divertor tiles with tungsten, which better withstands extreme heat. KSTAR has a target of 300 seconds at 100 million degrees by 2026, and AI-aided feedback control is planned to help reach it.

Tokamak Energy’s ST40: Private Sector Milestone

In March 2022, UK-based Tokamak Energy announced that its compact ST40 spherical tokamak had achieved a plasma ion temperature exceeding 100 million degrees Celsius — the threshold required for commercial fusion — making it the first privately funded spherical tokamak to do so. The result was verified by an independent Diagnostic Advisory Board and published through the Institute of Physics. ST40 also achieved a fusion triple product (the combined measure of temperature, density, and confinement time) of approximately 6 × 10¹⁸ m⁻³·keV·s, the highest by any private fusion company. By late 2025, further experiments improved this to approximately 8 × 10¹⁸ m⁻³·keV·s. Tokamak Energy developed SOPHIA, an in-house digital twin for ST40, which was trialled in late 2023 and fully integrated into operations in 2024.

Part Five: The Key Players

The Laboratories

Princeton Plasma Physics Laboratory (PPPL), USA: The anchor of American fusion AI research. William Tang led the FRNN disruption predictor and is building an AI-ML-Enabled Tokamak Digital Twin with NVIDIA. Egemen Kolemen’s group produced the 2024 tearing-avoidance breakthrough. Michael Churchill heads Digital Engineering and AI, leading the StellFoundry AI stellarator design project. PPPL’s STELLAR-AI platform, funded by the U.S. Department of Energy, partners with NVIDIA, Microsoft, MIT, and UKAEA.

Google DeepMind and EPFL Swiss Plasma Centre: Their TCV collaboration produced the landmark 2022 plasma control paper. In October 2025, DeepMind announced a partnership with Commonwealth Fusion Systems covering fast differentiable simulation and reinforcement-learning-based real-time control. DeepMind also released TORAX, an open-source differentiable tokamak simulator written in Python/JAX.

General Atomics, USA: Operates the DIII-D National Fusion Facility, which hosts perhaps the most advanced operational AI-enabled digital twin in fusion — built with NVIDIA Omniverse and serving over 700 scientists from more than 100 institutions worldwide.

MIT Plasma Science and Fusion Centre: Cristina Rea heads the Disruption Studies Group and has deployed data-driven stability metrics in real time across DIII-D, EAST (China), and KSTAR.

The Companies

Commonwealth Fusion Systems (CFS), USA: Spun out of MIT in 2018 and led by CEO Bob Mumgaard, CFS has raised nearly three billion dollars and is building SPARC, a compact high-field tokamak in Devens, Massachusetts. At CES in January 2026, CFS unveiled a comprehensive digital twin of SPARC developed with NVIDIA (Omniverse platform) and Siemens (Xcelerator, NX, and Teamcenter PLM systems). SPARC aims for first plasma in 2027, with a target fusion gain of Q > 2 and potentially Q > 10.

TAE Technologies, USA: Founded in 1998 and led by CEO Michl Binderbauer, TAE holds the longest-running AI-fusion partnership, having collaborated with Google since 2014. They co-developed the “Optometrist Algorithm” — a human-in-the-loop Bayesian optimisation approach published in Scientific Reports in July 2017 — which achieved more than 50 percent reduction in energy loss rate on the C-2U device and extended plasma lifetimes up to three times on the Norman (C-2W) machine. TAE has raised over 1.3 billion dollars and is building its Copernicus device targeting net energy by decade’s end.

Tokamak Energy, UK: Founded in 2009 as a spin-off from the Culham Centre for Fusion Energy, Tokamak Energy has raised approximately 335 million dollars and is building the ST80-HTS device at UKAEA’s Culham Campus. Their SOPHIA digital twin is now fully integrated into ST40 operations.

Helion Energy, USA: Led by CEO David Kirtley with chairman Sam Altman, Helion has raised over one billion dollars and, in February 2026, demonstrated deuterium-tritium fusion at 150 million degrees Celsius on its Polaris prototype — a private-sector temperature record. Microsoft has agreed to purchase Helion’s first fusion electricity, expected around 2028. Notably, Helion has no prominent public AI digital twin programme; its competitive edge relies on rapid physical prototyping across seven device generations.

Proxima Fusion, Germany: The first spin-out from the Max Planck Institute for Plasma Physics (2023), led by CEO Francesco Sciortino, Proxima is developing StarFinder, an AI-driven stellarator optimisation platform, backed by a 6.5-million-euro German government grant for “AI for Fusion Engineering.”

Part Six: The Honest Limits — What Hasn’t Worked and What AI Cannot Fix

The Sim-to-Real Gap

Every AI system trained in simulation faces the same fundamental question: will it work in the real world? Princeton’s Michael Churchill has stated plainly that simulations are not perfectly faithful to reality and that known deficiencies exist. DeepMind’s TCV success involved a relatively small tokamak with two-second plasma pulses and fifteen-minute cooldowns between shots. Scaling to ITER — where the stored energy in the plasma will exceed 350 megajoules, compared to roughly one megajoule on DIII-D — represents an extrapolation of more than two orders of magnitude that no AI system has yet attempted.

Cross-Machine Transfer Degrades

The best disruption prediction systems achieve area-under-the-curve scores of roughly 0.97 on the machine they were trained on. But performance drops substantially when transferred to different devices. Transfer learning from small to large tokamaks requires significant adaptation, and critically, ITER cannot afford to train AI on its own unmitigated disruptions — each one risks catastrophic damage to the reactor vessel.

ITER: The Cautionary Giant

ITER, the massive international fusion project being built in Cadarache, France, is both humanity’s greatest fusion ambition and its most sobering cautionary tale. Originally targeting first plasma in 2016 at a cost of approximately 5.9 billion euros, the project now plans research operations beginning in 2034 and full deuterium-tritium fusion in 2039, at an estimated total cost exceeding 25 billion euros. Director-General Pietro Barabaschi acknowledged in July 2024 that previous planning had been too optimistic. Key problems have included defective vacuum vessel welding, corroded thermal shield piping requiring the replacement of 23 kilometres of pipe, and interventions by French nuclear regulators.

The Problems AI Cannot Solve

Some of fusion’s hardest remaining challenges are physical realities that no algorithm can shortcut. No tritium breeding blanket has ever been tested in a fusion environment. Global tritium supply is only about 20 kilograms per year. Materials that can survive years of bombardment by 14 MeV neutrons — which cause swelling, embrittlement, and transmutation in structural metals — remain unproven because the test facilities do not yet exist. Recent analyses show that neutron damage creates high-energy tritium trapping sites that pose severe implications for tritium self-sufficiency. These are engineering and materials science challenges that AI accelerates understanding of but cannot circumvent.

Part Seven: Hype Check — When “Digital Twin” Is Marketing

Not everything called a “digital twin” in the fusion industry actually qualifies as one. A strict digital twin requires bidirectional real-time data flow between the physical system and its virtual counterpart, dynamic model updating, and a closed feedback loop. By this definition, perhaps only two or three implementations worldwide qualify as of early 2026.

Several prominent uses of the term are more aspirational than actual. ITER’s “digital twin” (developed via Bentley Systems) is primarily a 3D construction management model — engineering CAD for tracking components, not plasma physics. NVIDIA Omniverse-based platforms at UKAEA and Princeton are advanced visualisation environments moving toward digital twin capability but not yet fully realised. China’s HL-3 tokamak “digital twin” for vacuum chamber monitoring is essentially a sensor fusion and temperature display system.

The October 2024 APS Division of Plasma Physics mini-conference — the first formal academic gathering on the topic — introduced the term “intelligent digital twin” (iDT) to distinguish AI-enhanced systems from simpler models and emphasised that uncertainty quantification is essential to distinguish a credible digital twin from a deterministic simulation running alongside an experiment.

Part Eight: The Road Ahead

The near-term roadmap is packed with milestones. SPARC aims for first plasma in 2027. KSTAR targets 300 seconds at 100 million degrees by 2026 using AI-aided feedback. Helion plans to deliver its first commercial fusion electricity to Microsoft around 2028. The U.S. Department of Energy’s October 2025 Fusion Science and Technology Roadmap targets a Fusion Pilot Plant on the grid by the mid-2030s.

The Fusion Industry Association’s 2025 survey of 53 companies found that 53 percent predict electricity delivery by 2035 and 84 percent by the end of the 2030s. However, 83 percent of respondents identified funding as a major challenge, with the median additional capital needed per company at approximately 700 million dollars. Total private fusion investment had reached 9.77 billion dollars cumulative through mid-2025.

On the regulatory front, the U.S. Nuclear Regulatory Commission proposed in early 2026 a licensing rule treating fusion devices as byproduct-material facilities under a performance-based, technology-neutral framework — a significant reduction in regulatory uncertainty for startups compared to the framework that governs fission nuclear plants.

AI and digital twin technology is expected to evolve toward foundation models for plasma physics (large neural networks trained across data from multiple machines), real-time edge computing on FPGAs and custom accelerators, and standardised open interfaces merging legacy simulation codes with modern machine learning frameworks. NVIDIA’s emerging role as the platform provider — partnering simultaneously with CFS, General Atomics, UKAEA, and PPPL — positions it as de facto infrastructure for fusion digital twins.

Conclusion: Tools, Not Magic

AI digital twins in fusion occupy a space between genuine scientific breakthrough and aspirational marketing. The verified achievements are real and significant: DeepMind proved a single neural network could replace an entire conventional plasma control system. Princeton proved AI can predict and proactively avoid dangerous instabilities in real time. LLNL’s CogSim framework meaningfully guided the shots that achieved fusion ignition. Japan’s NIFS team demonstrated the first closed-loop digital twin control of fusion plasma.

But it is equally important to be honest about what AI has not done. The biggest fusion records — JET’s 69.26 megajoules, NIF’s Q ≈ 4.13, KSTAR’s 48-second sustained burn — were not primarily AI-driven achievements. The strict definition of a digital twin is met by perhaps only two or three implementations worldwide. And fusion’s hardest remaining problems — tritium self-sufficiency, materials survival under decades of neutron bombardment, and the enormous scaling leap from laboratory to reactor conditions — are physical challenges that no algorithm can shortcut.

The honest assessment is this: AI digital twins are becoming indispensable tools that will shave years off the development timeline. They are accelerating the pace of discovery in ways that matter enormously. But they are tools within a discipline that still faces unsolved engineering and physics challenges of extraordinary difficulty. The question is not whether AI will help deliver fusion — it clearly will. The question is whether the underlying physical obstacles can be overcome at all, on any timeline.

For now, the race continues. And for the first time in the long, humbling history of fusion research, the finish line feels closer than the starting blocks.

Behind The Story

This article was produced through a two-stage process. First, an extensive research phase drew on peer-reviewed publications (Nature, Nature Communications, Scientific Reports, Physics of Plasmas), official institutional announcements from PPPL, LLNL, DeepMind, EUROfusion, ITER, CFS, TAE Technologies, Tokamak Energy, and Helion Energy, as well as credible science journalism from MIT Technology Review, IEEE Spectrum, and Physics World. Second, all key claims were cross-referenced against multiple independent sources and corrected where discrepancies were found. For example, Tokamak Energy’s ST40 milestone of 100 million degrees Celsius is correctly dated to March 2022 (as confirmed by the company, Wikipedia, World Nuclear News, and Fusion Energy Insights), correcting some secondary sources that incorrectly place it in 2023. NIF’s achievement of Q > 1 via inertial confinement is distinguished from the fact that no magnetic confinement device has yet achieved Q > 1. Claims about “digital twin” implementations are assessed against the strict engineering definition (bidirectional data flow, dynamic updating, closed feedback loop) rather than accepting marketing language at face value. No inline citations are used; all facts are verifiable through the named institutions, publications, and dates provided throughout the text.