Stochastic simulations such as large-scale, spatiotemporal, age-structured epidemic models are computationally expensive at fine-grained resolution. We propose Spatiotemporal Neural Processes (STNP), a neural latent variable model to mimic the spatiotemporal dynamics of stochastic simulators. To further speed up training, we use a Bayesian active learning strategy to proactively query the simulator, gather more data, and continuously improve the model. Our model can automatically infer the latent processes which describe the intrinsic uncertainty of the simulator. This also gives rise to a new acquisition function based on latent information gain. Theoretical analysis demonstrates that our approach reduces sample complexity compared with random sampling in high dimension. Empirically, we demonstrate that our framework can faithfully imitate the behavior of a complex infectious disease simulator with a small number of examples, enabling rapid simulation and scenario exploration.
翻译:大规模、 时空、 年龄结构的流行病模型等随机模拟模型在精细分辨率下计算成本昂贵。 我们建议使用神经神经过程(STNP)这个神经潜伏变异模型来模拟随机模拟模拟模拟器的神经时空动态。 为了进一步加快培训, 我们使用贝叶斯积极的学习策略来主动查询模拟器, 收集更多的数据, 并不断改进模型。 我们的模型可以自动推断出描述模拟器内在不确定性的潜在过程。 这也产生了基于潜在信息增益的新获取功能。 理论分析表明,我们的方法比随机抽样的高度减少了样本复杂性。 我们的框架可以忠实地模仿复杂的传染病模拟器的行为, 并举几个例子, 能够进行快速模拟和情景探索 。