In the control room of the String Light Research Institute's Fusion‑Energy Center, Xiuxiu stood before the wraparound holographic console, her eyes fixed on the massive tokamak device "Suirenshi" at the center. Inside this ring‑shaped apparatus, over twenty meters in diameter, the temperature had risen to 150 million degrees Celsius—ten times hotter than the Sun's core. Under such extreme conditions, the deuterium‑tritium plasma churned violently, confined by the powerful toroidal magnetic field, emitting a dazzling blue glow. Dozens of display screens in the control room showed real‑time parameters of the plasma: density fluctuations, temperature distribution, magnetic‑field configuration, turbulence intensity—each piece of data signaling that the system was on the verge of losing control. This was the thirty‑ninth attempt to break the hundred‑second barrier for stable fusion‑reaction operation; the previous thirty‑eight had all failed at the final moment due to uncontrollable plasma turbulence. Xiuxiu took a deep breath; her gaze shifted to the right side of the control room, where a new control system flickered with neural‑network activity signals. This was the novel control system based on physics‑informed neural networks that her team had spent two years developing—today it would face its ultimate test.
Traditional plasma‑control methods relied on preset mathematical models and feedback‑control algorithms, but these approaches often struggled when confronted with the complex nonlinear behavior of plasma. Plasma turbulence, a problem that had plagued fusion research for decades, was like trying to capture flowing water with a fishing net—the harder one tried to control it, the more intense the turbulence became. Xiuxiu's breakthrough idea was to introduce a convolutional‑neural‑network architecture into plasma control, but not simply using black‑box deep learning; instead, she creatively embedded physical laws directly into the neural network's structure. The core concept of this physics‑informed neural network was to incorporate fundamental physical laws such as Maxwell's equations and fluid‑dynamics equations as constraints directly into the network's loss function, forcing the neural network to obey these basic physical rules during learning and prediction.
The console displayed the detailed architecture of the physics‑informed neural network. This was a deep convolutional neural network whose input layer received real‑time data from two thousand diagnostic probes inside the tokamak device, including magnetic‑field strength, plasma density, and temperature distribution. The hidden layers were not ordinary fully‑connected layers but specially designed physics‑constraint layers that forced the network's intermediate representations to satisfy the discrete form of Maxwell's equations. Specifically, the loss function was designed as:
$$
\mathcal{L} = \mathcal{L}_{\text{data}} + \lambda \mathcal{L}_{\text{physics}}
$$
where $\mathcal{L}_{\text{data}}$ is the conventional data‑fitting loss, $\mathcal{L}_{\text{physics}}$ is the physics‑constraint loss, and $\lambda$ is a trade‑off parameter. The specific form of the physics‑constraint loss is:
$$
\mathcal{L}_{\text{physics}} = \sum_{i=1}^{N} \left[ \left(\nabla \cdot \mathbf{E} - \frac{\rho}{\epsilon_0}\right)^2 + \left(\nabla \cdot \mathbf{B}\right)^2 + \left(\nabla \times \mathbf{E} + \frac{\partial \mathbf{B}}{\partial t}\right)^2 + \left(\nabla \times \mathbf{B} - \mu_0\mathbf{J} - \mu_0\epsilon_0\frac{\partial \mathbf{E}}{\partial t}\right)^2 \right]
$$
This design ensures that the neural network must obey the constraints of Maxwell's equations when learning and predicting the evolution of electromagnetic fields.
"Plasma current rising to fifteen mega‑amperes." The controller reported, his voice tense. This was a critical signal that the fusion reaction was approaching the ignition point. On the holographic display, one could clearly see strong turbulent structures beginning to emerge inside the plasma; these rotating vortices, like miniature storms, continuously battered the magnetic‑confinement boundary. The conventional control system immediately responded by adjusting the currents in the external coils in an attempt to suppress the turbulence, but the effect was limited. Turbulence intensity continued to rise, and readings from several key probes began to sound alarms.
"Activate the neural‑network control system." Xiuxiu ordered, her voice calm but firm.
Instantly, all the screens in the control room transformed. The previously chaotic turbulence data began to be parsed and understood by the neural network in real time. The special architecture of the convolutional neural network enabled it to recognize the multiscale structure of turbulence—from microscopic turbulence at the ion‑gyroradius scale to large‑scale instabilities at the device scale. Even more astonishingly, the predictions of the physics‑informed neural network not only relied on data patterns but also strictly adhered to the fundamental laws of plasma physics. The screen began to show the network's predictions of plasma evolution: within the next half‑second, intense magnetic reconnection would occur in a specific region, potentially leading to confinement loss.
"Adjust the current waveforms of control coils three, seven, and twelve." The neural‑network control system automatically issued commands. These instructions were not based on simple feedback control but were the results of the neural network's computation of optimal control strategies under physical constraints. The currents of the control coils began to change according to complex timing patterns; these changes excited specific waves in the plasma that interfered destructively with the existing turbulence.
Over the next few seconds, everyone in the control room held their breath, eyes locked on the main display. The turbulence‑intensity curve began to fluctuate, then steadily declined. The plasma boundary became clear and stable; the internal temperature distribution grew more uniform. The neural‑network control system continued to output exquisite control commands—it seemed to be engaged in a complex "dialogue" with the plasma, guiding it toward a more stable state through carefully designed magnetic‑field perturbations.
"All plasma parameters entering the stable range." The voice of the physics‑diagnostics lead was filled with incredulous delight. On the display, the green area representing plasma stability expanded steadily, while those warning red and yellow zones gradually disappeared. Even more heartening was that the fusion‑power‑output curve began to rise steadily—this meant the collision frequency of deuterium‑tritium nuclei was increasing; the fusion reaction was becoming more efficient.
Xiuxiu approached the console and called up the real‑time visualization of the neural network's internal state. In this visualization, one could clearly see how the network comprehended the complex behavior of the plasma. Convolutional layers recognized the spatial structure of turbulence; recurrent layers captured the temporal evolution; and physics‑constraint layers ensured that all this understanding conformed to basic physical laws. It was like installing an intelligent brain in the tokamak device—a brain that could "understand" plasma physics.
Time passed, second by second, and the fusion reaction continued to operate stably. When the clock showed the device had run continuously for over fifty seconds, suppressed murmurs of excitement began to ripple through the control room. This had already broken the historical record for this device, and all indicators showed the system was still performing perfectly.
"Detection of a neoclassical‑tearing‑mode seed." Suddenly, the warning system sounded an alert. This was one of the most dangerous instabilities in tokamak devices, having caused abrupt terminations in numerous experiments. The conventional control system immediately entered emergency mode, but the neural‑network control system displayed remarkable composure. It did not adopt aggressive suppression measures; instead, it output an extremely fine‑grained set of control signals that excited a series of minute magnetic perturbations in the plasma. These perturbations acted like precise surgical procedures, dissolving the tearing mode before it could fully form.
"Turbulence‑suppression efficiency reaching ninety‑three percent." A data analyst reported an astounding figure. This far exceeded the best record achieved by traditional methods, and even surpassed the team's most optimistic expectations. Under the neural network's control, the plasma resembled a tamed beast—still brimming with energy, yet operating orderly according to human will.
When the clock struck the hundred‑second mark, the control room erupted in thunderous applause and cheers. People hugged one another; some were moved to tears. A hundred seconds of stable operation was more than just a number—it represented humanity's most crucial step toward mastering fusion energy. At this duration, the net energy gain of the fusion reaction had reached a significant level, proving in principle the commercial feasibility of fusion power generation.
At this historic moment, Xiuxiu's communicator chimed. It was a video call from Yue'er, who had just woken up in the hospital. On the screen, Yue'er's complexion was still somewhat pale, but her eyes shone with intelligence: "I just reviewed the experimental data. You used AI to tame the Sun." This statement deeply moved Xiuxiu. She gazed at the celebrating crowd in the control room and replied softly, "No, we have merely learned the language for conversing with stars."
Over the next several hours, the team conducted a detailed analysis of the experimental data. The results were stunning: the neural‑network control system not only achieved unprecedented stable control but also discovered some new plasma‑stable states. These stable states had not been predicted by conventional theoretical models but were discovered through the data‑driven approach of the neural network, and—guaranteed by the physical constraints—were confirmed to obey fundamental physical laws. This demonstrated a tremendous advantage of physics‑informed neural networks: they can learn from data while never violating basic physical laws, thereby uncovering new phenomena that might be missed by purely theoretical derivations or purely data‑driven methods.
Late at night, when only the core team members remained in the control room, Xiuxiu led a new round of discussion. The significance of this breakthrough extended far beyond the realization of fusion energy; it showcased a brand‑new paradigm for scientific research—neural‑network physics. In this paradigm, artificial intelligence is no longer a black‑box tool but an intelligent agent deeply integrated with physical laws—capable of processing vast amounts of experimental data while strictly adhering to the laws of nature.
Xiuxiu wrote in the experiment log: "Today, we not only achieved stable control of fusion reactions; more importantly, we pioneered a new method for understanding complex physical systems. Neural‑network physics combines the perceptual capabilities of AI with the rational constraints of physical laws, enabling us to tackle systems too complex to be handled by traditional approaches. From plasma turbulence to climate systems, from neuroscience to cosmology, this new method may change how we investigate the natural world."
When the first rays of dawn filtered through the control‑room windows, Xiuxiu was still analyzing experimental data. She knew this was only the beginning. The field of neural‑network physics held countless unanswered questions: How can physical constraints be better embedded in neural‑network architectures? How should multiphysics‑coupled complex systems be handled? How can the interpretability of neural networks be ensured? Yet, regardless of these challenges, today's results had already proved that this path is full of promise.
At that day's press conference, Xiuxiu announced this breakthrough to the world. She emphasized in particular: "This is not a victory of artificial intelligence; it is a triumph of human intellect. The tool we created not only helps us solve difficult problems; more importantly, it helps us understand the laws of nature more profoundly. Guided by neural‑network physics, we are entering a new era of deep fusion between science and intelligence."