Abstract We initiate a way of generating effective field theories (EFT) models by the computer, satisfying both experimental and theoretical constraints. We use Generative Adversarial Networks (GAN) and display generated instances which go beyond the examples known to the machine during training. As a starting point, we apply this idea to the generation of supersymmetric field theories with a single field. We find cases where the number of minima in the generated scalar potential is different from values found in the training data. We comment on potential further applications of this framework.

中文翻译：

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用于生成 EFT 模型的 GAN

摘要 我们开创了一种通过计算机生成有效场论 (EFT) 模型的方法，同时满足实验和理论约束。我们使用生成对抗网络 (GAN) 并显示生成的实例，这些实例超出了机器在训练期间已知的示例。作为起点，我们将这个想法应用于生成具有单个场的超对称场论。我们发现生成的标量势中的最小值数量与训练数据中发现的值不同的情况。我们评论该框架的潜在进一步应用。