论文标题
应对模拟器，并不总是返回
Coping With Simulators That Don't Always Return
论文作者
论文摘要
确定性模型是现实的近似值，易于解释，通常比随机替代方案更容易构建。不幸的是，由于自然是反复无常的，因此在实践中确定性模型永远无法完全解释观察数据。需要添加观察和过程噪声，以适应确定性模型的随机表现，以便它们能够解释和外推从嘈杂的数据中。我们调查并解决了通过将过程噪声添加到确定性模拟器中而产生的计算效率低下，这些模拟器无法返回某些输入；我们形容为“脆性”的属性。我们展示了如何训练有条件的归一流流动以提出扰动，以使模拟器以很高的概率成功，从而提高了计算效率。
Deterministic models are approximations of reality that are easy to interpret and often easier to build than stochastic alternatives. Unfortunately, as nature is capricious, observational data can never be fully explained by deterministic models in practice. Observation and process noise need to be added to adapt deterministic models to behave stochastically, such that they are capable of explaining and extrapolating from noisy data. We investigate and address computational inefficiencies that arise from adding process noise to deterministic simulators that fail to return for certain inputs; a property we describe as "brittle." We show how to train a conditional normalizing flow to propose perturbations such that the simulator succeeds with high probability, increasing computational efficiency.
