We consider machine-learning of time-dependent quantities of interest derived from solution trajectories of parabolic partial differential equations. For large-scale or long-time integration scenarios, where using a full order model (FOM) to generate sufficient training data is computationally prohibitive, we propose an adaptive hierarchy of intermediate Reduced Basis reduced order models (ROM) to augment the FOM training data by certified ROM training data required to fit a kernel model.

翻译：我们考虑从抛物线部分差异方程的解答轨迹中,根据时间来机化学习一定数量的利息。 对于大规模或长期整合情景,如果使用完整订单模型(FOM)生成足够的培训数据在计算上令人望而却步,那么我们建议对中度减底订单减价模型(ROM)进行一个适应性等级,通过符合内核模型所需的经认证的ROM培训数据来补充FOM培训数据。