We present a probabilistic deep learning methodology that enables the construction of predictive data-driven surrogates for stochastic systems. Leveraging recent advances in variational inference with implicit distributions, we put forth a statistical inference framework that enables the end-to-end training of surrogate models on paired input-output observations that may be stochastic in nature, originate from different information sources of variable fidelity, or be corrupted by complex noise processes. The resulting surrogates can accommodate high-dimensional inputs and outputs and are able to return predictions with quantified uncertainty. The effectiveness our approach is demonstrated through a series of canonical studies, including the regression of noisy data, multi-fidelity modeling of stochastic processes, and uncertainty propagation in high-dimensional dynamical systems.
翻译:我们提出了一个预测数据驱动的替代代孕器的概率深层次学习方法,该方法有助于为蒸汽系统建造预测数据驱动的代孕器。利用最近通过隐含分布进行变异推论的进展,我们提出了一个统计推论框架,使代孕模型的端到端培训能够进行对口投入-产出观测,这些观测在性质上可能是随机的,来自不同信息源的可变忠诚,或因复杂的噪音过程而腐蚀。由此产生的代孕器能够容纳高维投入和产出,并能够返回具有量化不确定性的预测。我们的方法的有效性通过一系列的罐头研究得到证明,包括噪音数据的回归、多纤维性模型的随机过程以及高维动态系统中的不确定性传播。