In nuclear fusion reactors, plasmas are heated to very high temperatures of more than 100 million kelvin and, in so-called tokamaks, they are confined by magnetic fields in the shape of a torus. Light nuclei, such as deuterium and tritium, undergo a fusion reaction that releases energy, making fusion a promising option for a sustainable and clean energy source. Tokamak plasmas, however, are prone to disruptions as a result of a sudden collapse of the system terminating the fusion reactions. As disruptions lead to an abrupt loss of confinement, they can cause irreversible damage to present-day fusion devices and are expected to have a more devastating effect in future devices. Disruptions expected in the next-generation tokamak, ITER, for example, could cause electromagnetic forces larger than the weight of an Airbus A380. Furthermore, the thermal loads in such an event could exceed the melting threshold of the most resistant state-of-the-art materials by more than an order of magnitude. To prevent disruptions or at least mitigate their detrimental effects, empirical models obtained with artificial intelligence methods, of which an overview is given here, are commonly employed to predict their occurrence—and ideally give enough time to introduce counteracting measures.

在核聚变反应堆中，等离子体被加热到超过 1 亿开尔文的非常高的温度，在所谓的托卡马克中，它们受到环形磁场的限制。轻原子核，如氘和氚，经过聚变反应释放能量，使聚变成为可持续和清洁能源的有希望的选择。然而，托卡马克等离子体容易因系统突然崩溃而中断，从而终止聚变反应。由于中断会导致限制的突然丧失，它们可能会对当今的聚变设备造成不可逆转的损害，并且预计会对未来的设备产生更具破坏性的影响。例如，下一代托卡马克 ITER 中预期的中断可能会产生比空客 A380 的重量更大的电磁力。此外，在这种情况下，热负荷可能会超过最先进的最先进材料的熔化阈值一个数量级以上。为了防止干扰或至少减轻其不利影响，通过人工智能方法获得的经验模型（此处给出概述）通常用于预测它们的发生 - 理想情况下，有足够的时间来引入对抗措施。