Can Milliseconds of AI Control Make Fusion Reactors More Manageable?

Fusion energy has long promised a future of abundant, clean power, yet the path to commercial reactors remains obstructed by a singular, formidable challenge: plasma instability. The superheated, ionized gas that fuels fusion reactions is inherently chaotic, prone to disruptive events that can extinguish the reaction or damage the reactor vessel in milliseconds. Traditional control systems, bound by the latency of human oversight and conventional algorithms, often react too slowly to prevent these catastrophic instabilities, relegating fusion to the realm of laboratory curiosity rather than a viable power source.

Enter the PACMAN framework, an artificial intelligence system designed to act as a real-time pilot for plasma stability. Developed by researchers and reported by Phys.org, PACMAN represents a paradigm shift in fusion control, prioritizing the speed of decision-making over the precision of a perfect, calculated response. This approach acknowledges a fundamental truth of fusion physics: when a plasma begins to destabilize, a swift, approximate correction is infinitely more valuable than a delayed, perfect one. The framework's ability to make split-second control decisions could be the critical bridge between experimental success and the operational reliability demanded by a commercial power grid.

This analysis delves into the technical architecture of PACMAN, exploring how machine learning models are trained to predict and counteract plasma disruptions. We will examine the physics of plasma instability, the mathematics of rapid control theory, and the engineering hurdles of integrating AI into the high-stakes environment of a fusion reactor. The core message is clear: the future of fusion energy may hinge less on generating the reaction and more on mastering the millisecond-level art of keeping it under control.

The Imperative of Speed in Plasma Control

Fusion reactors operate under conditions that are, by any measure, extreme. The plasma core reaches temperatures exceeding 150 million degrees Celsius, hotter than the center of the Sun, yet it must be confined within magnetic fields without touching the reactor walls. This delicate balance is perpetually threatened by instabilities such as edge-localized modes (ELMs) and neoclassical tearing modes (NTMs), which can erupt without warning.

When an instability occurs, the energy confinement degrades rapidly, and the plasma can disrupt, releasing its stored energy onto the reactor components in a violent event. The timescale for these disruptions is measured in milliseconds, a window so narrow that human operators are effectively blind to the process. Conventional control algorithms, while faster than humans, still suffer from computational latency that can render their corrective actions moot by the time they are executed.

The Physics of Disruption Events

Plasma disruptions are not singular phenomena but a cascade of interconnected failures. A minor perturbation in the magnetic field can grow into a large-scale instability, leading to a sudden loss of confinement and a rapid current quench. The energy released during this process can exert forces equivalent to a jet engine on the reactor structure, necessitating costly repairs and extended downtime.

Understanding the predictive signatures of these disruptions is a primary focus of fusion research. Machine learning models like PACMAN are trained on vast datasets of historical plasma behavior, learning to recognize the subtle precursors that precede a major instability. This predictive capability is the first line of defense, allowing the control system to anticipate a problem before it fully manifests.

The challenge is that plasma behavior is not purely deterministic; it is influenced by a multitude of interacting variables, including density, temperature, and magnetic field configuration. A model that can process these variables in real-time and output a control action within the required latency is a monumental engineering achievement. PACMAN's architecture is designed specifically to meet this challenge, prioritizing inference speed without sacrificing the accuracy needed for effective control.

The physics of disruption events also involves the interaction of the plasma with the surrounding structures. When a disruption occurs, the rapid change in plasma current induces eddy currents in the reactor vessel, creating forces that can cause structural fatigue over time. Mitigating these events is not just about preserving the reaction but also about protecting the multi-billion-dollar investment in the reactor itself.

Recent experiments have shown that AI-driven control can reduce the frequency of disruptions by anticipating and mitigating the conditions that lead to them. The PACMAN framework builds on this foundation, offering a more granular and rapid response mechanism. The ultimate goal is a reactor that can operate continuously without the threat of a disruptive event, a prerequisite for economic viability.

Latency as the Ultimate Bottleneck

In the world of real-time control, latency is the enemy of stability. The time it takes for a sensor to measure a plasma parameter, for the data to be processed, and for a control actuator to respond is the total reaction time of the system. For fusion plasmas, this total latency must be kept below a few milliseconds to effectively counteract the fastest-growing instabilities.

Traditional control systems, based on physics-based models, often require significant computational resources to solve the complex equations governing plasma behavior. These calculations can take longer than the instability timescale, rendering them useless for real-time control. AI models, once trained, can perform inference in microseconds, offering a speed advantage of several orders of magnitude.

The PACMAN framework leverages this speed advantage by using a neural network that has been trained offline on simulation and experimental data. The network's architecture is optimized for fast inference, using techniques like quantization and pruning to reduce computational overhead. The result is a control system that can make decisions in under a millisecond, well within the required latency budget.

However, speed alone is insufficient; the control action must also be appropriate for the current state of the plasma. PACMAN addresses this by using a hierarchical control structure, where a fast, approximate model provides immediate corrective actions while a slower, more detailed model refines the strategy over a longer timescale. This hybrid approach balances the need for rapid response with the desire for optimal control.

The engineering challenge of reducing latency extends beyond the AI model itself. The sensors, data acquisition systems, and actuators must all be optimized for minimal delay. This requires a holistic approach to system design, where every component is evaluated for its contribution to the overall reaction time. PACMAN's success is as much a testament to systems engineering as it is to machine learning.

Architecture of the PACMAN Framework

PACMAN, an acronym for Predictive and Adaptive Control for Magnetic Actuation and Navigation, is a sophisticated AI framework engineered for the unique demands of fusion plasma control. Its architecture is a departure from monolithic neural networks, instead employing a modular design that separates the tasks of prediction, decision-making, and actuation. This separation allows each component to be optimized independently for speed and accuracy.

The core of PACMAN is a deep reinforcement learning (RL) agent that learns optimal control policies through interaction with a simulated plasma environment. The RL agent is trained to maximize a reward function that penalizes plasma instability and rewards stable operation. Through millions of simulated episodes, the agent develops an intuitive understanding of how to manipulate magnetic coils to maintain confinement.

Reinforcement Learning for Control Policies

Reinforcement learning is particularly well-suited for control problems where the optimal strategy is not known a priori. In the context of fusion, the RL agent must learn to navigate a high-dimensional state space, where each state represents the current configuration of the plasma and the reactor. The agent's actions are the voltages applied to the magnetic coils, which shape and position the plasma.

The training process for PACMAN involves a digital twin of the fusion reactor, a high-fidelity simulation that accurately models plasma physics. The RL agent interacts with this digital twin, receiving rewards for maintaining stability and penalties for allowing disruptions to occur. Over time, the agent's policy converges toward an optimal strategy that balances aggressive control with the risk of overcorrection.

One of the key innovations of PACMAN is its use of a recurrent neural network (RNN) to process time-series data from plasma sensors. Unlike a standard feedforward network, an RNN maintains an internal state that captures the history of past observations. This temporal memory is crucial for predicting instabilities that develop over several milliseconds, as it allows the model to identify evolving patterns.

The RL agent's policy is not static; it is continuously updated based on new data from the actual reactor. This online learning capability allows PACMAN to adapt to changing reactor conditions, such as wall conditioning or component degradation. The framework can also be transferred between different fusion devices, with the pre-trained policy serving as a starting point for fine-tuning on a new reactor.

The computational efficiency of the PACMAN policy is achieved through a process called knowledge distillation. A large, complex "teacher" network is trained to achieve high accuracy, and its knowledge is then transferred to a smaller, faster "student" network that can run in real-time. The student network retains most of the teacher's accuracy while operating at a fraction of the computational cost.

Sensor Fusion and Real-Time Data Processing

A fusion reactor is equipped with a vast array of diagnostics, each measuring a different aspect of the plasma. These include magnetic probes, interferometers for density measurement, and Thomson scattering systems for temperature profiles. Fusing this disparate data into a coherent picture of the plasma state is a significant challenge, particularly under the constraint of millisecond latency.

PACMAN employs a sensor fusion architecture that pre-processes raw diagnostic signals into a standardized feature vector. This pre-processing includes noise filtering, signal conditioning, and feature extraction, all performed on dedicated hardware such as FPGAs or GPUs. The goal is to minimize the time between a physical event occurring in the plasma and the corresponding feature being available to the AI model.

The choice of features is critical for the performance of the control system. PACMAN uses a combination of raw signals and derived quantities, such as the plasma's internal inductance and the mode amplitude of specific instabilities. These features are selected based on their predictive power, identified through a combination of physics insight and automated feature selection algorithms.

Data synchronization is another critical aspect of real-time processing. Diagnostics sample at different rates, and their signals may be subject to different delays. PACMAN includes a synchronization module that aligns all incoming data to a common time base, ensuring that the AI model receives a consistent snapshot of the plasma state. This synchronization is essential for the temporal memory of the RNN to function correctly.

The output of the PACMAN model is a set of control commands, typically voltage requests for the magnetic coil power supplies. These commands are transmitted to the actuators via a high-speed, deterministic network, ensuring that the control action is applied at the precise moment it is needed. The entire pipeline, from sensor measurement to actuator response, is designed to operate within a strict latency budget.

Mathematical Foundations of Rapid Control

The control problem in fusion is fundamentally a mathematical challenge of optimizing actions under strict time constraints. The plasma state evolves according to the magnetohydrodynamic (MHD) equations, a set of nonlinear partial differential equations that describe the behavior of conducting fluids in magnetic fields. Solving these equations in real-time is computationally prohibitive, necessitating the use of reduced-order models.

AI models like PACMAN serve as ultra-fast approximations of these complex dynamics. The training process can be viewed as a form of system identification, where the neural network learns to map the relationship between control inputs and plasma outputs. The mathematical framework of optimal control theory provides the foundation for evaluating the performance of these learned policies.

Optimal Control Theory in Milliseconds

Optimal control theory seeks to find a control law that minimizes a cost function over a given time horizon. For a linear system, the optimal solution is given by the Linear Quadratic Regulator (LQR), which provides a closed-form feedback law. However, plasma dynamics are highly nonlinear, rendering LQR approaches inadequate for all but the most simplified scenarios.

Model Predictive Control (MPC) is a more sophisticated approach that solves an optimization problem at each time step, using a model of the system to predict future states. While MPC can handle nonlinearities, its computational cost is often too high for millisecond-scale control. PACMAN effectively learns an approximation of an MPC policy, capturing its benefits without the computational burden.

The mathematical formulation of the control problem involves minimizing a cost function that penalizes deviations from a desired plasma state. This cost function typically includes terms for the plasma's kinetic energy, the magnetic energy, and the control effort. The RL agent learns to minimize the expected cumulative cost over time, effectively solving a stochastic optimal control problem.

One of the key mathematical challenges is the curse of dimensionality. The state of a fusion plasma is described by a continuous field, which in principle has infinite dimensions. PACMAN addresses this by using a finite set of carefully chosen features that capture the essential dynamics of the instabilities. This dimensionality reduction is guided by both physics intuition and data-driven techniques like principal component analysis.

The stability of the control system is another critical mathematical consideration. A control law that is too aggressive can destabilize the plasma, while one that is too passive may fail to prevent disruptions. PACMAN's training process explicitly incorporates stability constraints, ensuring that the learned policy does not induce oscillations or other undesirable behaviors.

Deriving the Control Law for a Simplified Plasma Model

To illustrate the mathematical principles, consider a simplified model of a single instability mode with amplitude ##[a(t)]##. The dynamics can be described by a linear ordinary differential equation, where the growth rate ##[\gamma]## is counteracted by a control actuator with effectiveness ##[u(t)]##. The goal is to drive the amplitude to zero as quickly as possible.

The system dynamics are given by the equation:

Here, ##[\gamma > 0]## represents the instability growth rate, and ##[u(t)]## is the control input. Without control, the amplitude grows exponentially, leading to a disruption. The control problem is to choose ##[u(t)]## to minimize the amplitude over time, subject to constraints on the maximum control effort.

A simple proportional control law, where the control input is proportional to the measured amplitude, can be analyzed analytically. Substituting ##[u(t) = K a(t)]## into the dynamics yields a closed-loop system with an effective growth rate of ##[\gamma - K]##. If the gain ##[K > \gamma]##, the closed-loop system is stable, and the amplitude decays exponentially.

The time constant of the closed-loop system is given by ##[\tau = 1/(K - \gamma)]##. To achieve a decay time of 1 millisecond, the required gain is ##[K = \gamma + 1000]##. This simple calculation illustrates the need for high-bandwidth control, as the gain must significantly exceed the instability growth rate to achieve rapid stabilization.

In practice, the control law is more complex, involving multiple actuators and state variables. However, the fundamental principle remains: the control system must have sufficient authority and bandwidth to counteract the fastest-growing instabilities. PACMAN's neural network learns a nonlinear control law that generalizes this principle across the full range of plasma operating conditions.

Training Methodologies and Simulation Environments

The development of a robust AI control system requires a training environment that accurately reflects the physics of a real fusion reactor. PACMAN is trained in a simulated environment that uses advanced MHD codes to model plasma behavior. These simulations are computationally intensive, requiring significant high-performance computing resources to generate the training data.

The training process is iterative, with the RL agent improving its policy through repeated interactions with the simulation. Each episode begins with a randomized initial plasma state, and the agent must maintain stability for a fixed duration. The reward function is designed to provide dense feedback, rewarding the agent for small improvements in stability and penalizing disruptions heavily.

Simulation-to-Reality Transfer

One of the greatest challenges in applying AI to physical systems is the gap between simulation and reality. A model trained in a perfect simulation may fail when deployed on a real reactor, where sensors are noisy, actuators have finite bandwidth, and the plasma behaves in ways not captured by the model. PACMAN addresses this challenge through a technique called domain randomization.

Domain randomization involves varying the parameters of the simulation during training, such as the plasma density, temperature, and magnetic field strength. By exposing the agent to a wide range of conditions, the learned policy becomes robust to variations that may be encountered in the real world. The agent learns to focus on the fundamental dynamics of instability rather than overfitting to specific simulation parameters.

Another critical technique is the use of a high-fidelity "reality gap" model. This model incorporates detailed models of sensor noise, actuator dynamics, and other non-ideal behaviors that are present in a real reactor. Training the agent against this reality gap model helps to ensure that the learned policy is not overly optimistic about the precision of its measurements or the speed of its actuators.

The transfer from simulation to reality is also facilitated by a process called fine-tuning. Once the agent has been trained in simulation, it is deployed on the real reactor in a safe, low-power mode. The agent's policy is then updated using real-world data, allowing it to adapt to any discrepancies between the simulation and reality. This online learning capability is a key differentiator of PACMAN.

The ultimate validation of PACMAN will be its performance on a full-scale fusion reactor, such as ITER or SPARC. These reactors operate at power levels where disruptions are not just an inconvenience but a potential safety hazard. The AI framework must demonstrate reliable, consistent control over extended periods before it can be trusted with the operation of a commercial power plant.

Benchmarking Against Conventional Controllers

To quantify the benefits of PACMAN, its performance is benchmarked against conventional control systems. These benchmarks include metrics such as the number of disruptions avoided, the root-mean-square error of plasma parameters from their target values, and the total control effort expended. The results consistently show that PACMAN outperforms conventional controllers on all these metrics.

In simulated disruption scenarios, PACMAN successfully avoids disruptions in over 95% of cases, compared to approximately 70% for conventional controllers. The AI framework also achieves tighter regulation of plasma parameters, with a 30% reduction in error compared to the baseline. These improvements translate directly into reduced wear on reactor components and increased availability for scientific experiments.

The speed advantage of PACMAN is most evident in its response to fast-growing instabilities. Conventional controllers, limited by their computational latency, often react too late to prevent a disruption. PACMAN's sub-millisecond response time allows it to apply corrective action before the instability reaches a critical amplitude, effectively nipping the problem in the bud.

However, benchmarking also reveals the limitations of the current framework. PACMAN's performance degrades in scenarios that are significantly outside its training distribution, such as a new type of instability that was not present in the training data. This highlights the need for continuous learning and adaptation, as the reactor's operating conditions evolve over time.

The economic case for AI control is compelling. Disruptions in a reactor like ITER could cause damage requiring months of repair, costing hundreds of millions of dollars. By reducing the frequency and severity of disruptions, PACMAN can significantly improve the economic viability of fusion energy, moving it closer to the goal of commercial power generation.

Engineering Challenges and Hardware Integration

Deploying an AI control system on a fusion reactor is not merely a software problem; it requires seamless integration with the reactor's hardware and control infrastructure. The PACMAN framework must interface with the reactor's existing control system, which manages everything from magnetic coils to auxiliary heating systems. This integration presents significant engineering challenges, particularly regarding safety and reliability.

The real-time requirements of PACMAN demand specialized hardware. Standard CPUs are often too slow for sub-millisecond inference, so PACMAN is deployed on FPGAs or GPUs, which can perform the necessary matrix operations in parallel. The choice of hardware involves a trade-off between computational power, power consumption, and reliability, all of which are critical in the harsh environment of a fusion reactor.

Real-Time Inference on Edge Hardware

Field-Programmable Gate Arrays (FPGAs) are a popular choice for real-time AI inference due to their low latency and deterministic behavior. Unlike GPUs, which are optimized for throughput, FPGAs can be configured to perform a specific neural network architecture with minimal latency. PACMAN's student network is compiled into an FPGA bitstream, creating a dedicated hardware accelerator for the control policy.

The process of compiling a neural network to an FPGA involves quantization, where the network's weights and activations are converted from floating-point to fixed-point representations. This reduces the computational resources required but can introduce numerical errors. PACMAN's training process accounts for this quantization, ensuring that the network remains accurate even with reduced precision.

Graphics Processing Units (GPUs) offer an alternative platform for real-time inference, providing higher flexibility than FPGAs. Modern GPUs can achieve inference times of under a millisecond for small networks, making them suitable for PACMAN's requirements. The choice between FPGA and GPU depends on the specific reactor's infrastructure and the need for reconfigurability versus absolute minimum latency.

The hardware must also be ruggedized to operate in the electromagnetic environment of a fusion reactor. The strong magnetic fields and radiation can interfere with electronics, necessitating shielding and careful circuit design. PACMAN's hardware is housed in shielded enclosures, and all communication links are fiber-optic to avoid electromagnetic interference.

Redundancy is a critical design principle for safety-critical control systems. PACMAN is deployed on multiple redundant hardware units, each running the same inference algorithm. A voting mechanism compares the outputs of the redundant units and selects the majority decision, ensuring that a single hardware failure does not compromise the control system.

Safety Protocols and Fail-Safe Mechanisms

The integration of AI into a safety-critical system like a fusion reactor raises important questions about trust and accountability. How can operators be confident that the AI will not make a catastrophic error? PACMAN addresses this through a layered safety architecture that includes both hardware and software safeguards.

The first layer of safety is a set of hard limits on the control actions. Regardless of what the AI recommends, the control system will not apply voltages that exceed the physical limits of the magnetic coils. These limits are enforced by hardware interlocks that operate independently of the AI software, providing a final line of defense against erroneous commands.

The second layer is a "safety wrapper" that monitors the AI's recommendations for anomalous behavior. If the AI suggests a control action that is wildly different from what is expected, the safety wrapper can override the recommendation and revert to a conservative, pre-programmed response. This wrapper is designed to catch errors that may arise from out-of-distribution inputs or software bugs.

The third layer is a graceful degradation strategy. If the AI system detects that it is operating outside its reliable envelope, it can initiate a controlled shutdown of the plasma. This is preferable to an uncontrolled disruption, as it allows the reactor to be brought to a safe state in a controlled manner. The AI is trained to recognize when it is uncertain and to err on the side of caution.

Finally, the entire PACMAN system is subject to rigorous testing and certification before it is allowed to control a real reactor. This includes hardware-in-the-loop testing, where the AI system is connected to a real-time simulation of the reactor, and fault injection testing, where various failure modes are simulated to verify the safety mechanisms. Only after passing these tests is PACMAN granted authority to control the plasma.

The Future of AI-Guided Fusion Energy

The development of PACMAN represents a significant milestone in the quest for practical fusion energy, but it is just the beginning of a broader transformation. As AI techniques continue to advance, their application to fusion control will become more sophisticated, enabling reactors to operate with unprecedented efficiency and reliability. The integration of AI is not a luxury but a necessity for the commercialization of fusion power.

The lessons learned from PACMAN extend beyond fusion, offering insights into the control of other complex, fast-acting physical systems. The principles of rapid AI inference, simulation-to-reality transfer, and layered safety are applicable to fields as diverse as autonomous vehicles, chemical process control, and high-energy physics experiments. PACMAN is a pioneering example of how AI can be trusted with real-time control of critical infrastructure.

Scaling AI Control to Commercial Reactors

Commercial fusion reactors will operate at a scale and power level far exceeding current experimental devices. This scaling presents new challenges for AI control, including the need to manage a larger number of actuators and sensors, and to operate continuously for extended periods. PACMAN's architecture is designed to be scalable, with the modular design allowing for the addition of new control modules as the reactor's complexity increases.

The training of AI models for commercial reactors will require even more sophisticated simulations, incorporating the full complexity of a power plant, including the balance of plant systems. These simulations will need to run faster than real-time to allow for extensive training, requiring advances in both hardware and algorithmic efficiency. The use of surrogate models, which approximate the behavior of the full simulation, will be essential for making this training tractable.

Another key challenge is the need for the AI to operate autonomously for long periods without human intervention. This requires the AI to handle a wide range of fault scenarios, from minor sensor glitches to major component failures. The AI must be able to diagnose the problem, determine the appropriate response, and execute that response without exceeding the safety limits of the reactor.

The economic model for AI-controlled fusion reactors is also evolving. The cost of the AI control system is a small fraction of the total reactor cost, yet it can have a disproportionate impact on the reactor's availability and performance. Investors are increasingly recognizing that AI is a key enabling technology for fusion, and funding for AI-driven control research is growing accordingly.

The regulatory landscape for AI-controlled nuclear facilities is still in its infancy. Regulators will need to develop frameworks for certifying AI systems that control fusion reactors, ensuring that they meet the same safety standards as conventional control systems. This will require close collaboration between AI developers, reactor designers, and regulatory bodies to establish clear guidelines and best practices.

Beyond Control: AI in Fusion Research and Design

The application of AI to fusion extends beyond real-time control, encompassing areas such as reactor design, materials science, and operational planning. AI can be used to optimize the design of magnetic coils, to discover new materials that can withstand the extreme conditions inside a reactor, and to plan experimental campaigns that maximize the scientific output of a device.

In the realm of reactor design, AI-driven optimization can explore a much larger design space than traditional methods. Generative models can propose novel reactor geometries that may offer improved performance, and these designs can be rapidly evaluated using fast surrogate models. This approach has the potential to accelerate the development of next-generation fusion devices significantly.

AI is also being used to analyze the vast amounts of data generated by fusion experiments. Machine learning algorithms can identify subtle patterns in the data that may indicate new physics phenomena or early warning signs of equipment degradation. This data-driven discovery process is complementing traditional physics-based analysis, leading to new insights into plasma behavior.

The integration of AI into the entire fusion development lifecycle, from design to operation, is creating a new paradigm for the field. This "AI-first" approach is attracting a new generation of researchers and engineers, who are combining expertise in machine learning with a deep understanding of plasma physics. The result is a vibrant and rapidly advancing field that is bringing fusion energy closer to reality.

The ultimate vision is a fully autonomous fusion power plant, where AI systems manage every aspect of operation, from startup to shutdown, with minimal human oversight. While this vision is still decades away, the PACMAN framework is a critical first step. It demonstrates that AI can be trusted with the fast, complex, and safety-critical task of controlling a fusion plasma, paving the way for the clean energy future that fusion promises.

Conclusion: The Millisecond Advantage

The PACMAN framework crystallizes a fundamental insight about fusion energy: the path to commercial viability is paved with milliseconds. In the race between plasma instability and corrective action, speed is not merely an advantage; it is the defining parameter. AI's capacity for sub-millisecond inference transforms the control problem from a reactive struggle into a proactive management of plasma dynamics.

The implications of this work extend far beyond the laboratory. Every disruption avoided is a step toward a reactor that can operate with the reliability demanded by the electrical grid. The economic calculus of fusion energy is fundamentally altered when AI can prevent the costly damage and downtime associated with plasma disruptions. PACMAN is not just a technical achievement; it is an economic enabler.

The journey from experimental device to commercial power plant is long and fraught with challenges, but AI control is emerging as a critical tool for navigating that path. The PACMAN framework demonstrates that the fusion community is willing to embrace new methodologies and that the integration of AI into the most demanding physical systems is not only possible but essential. The future of fusion is not just about hotter plasmas and stronger magnets; it is about smarter, faster control.

As research progresses, the fusion community will continue to refine AI frameworks like PACMAN, pushing the boundaries of what is possible in real-time control. The collaboration between plasma physicists and machine learning experts is yielding dividends that were unimaginable just a decade ago. The millisecond advantage provided by AI is a testament to the power of interdisciplinary innovation.

The promise of clean, abundant fusion energy has always been tantalizingly close, yet perpetually out of reach. With AI-guided control systems, that promise is becoming more tangible. The question is no longer whether fusion can work, but whether we can control it well enough to make it a practical reality. PACMAN provides a resounding answer: with AI at the helm, we can.