Multiphysics simulations that involve multiple coupled physical phenomena
quickly become computationally expensive. This imposes challenges for
practitioners aiming to find optimal configurations for these problems
satisfying multiple objectives, as optimization algorithms often require
querying the simulation many times. This paper presents a methodological
framework for training, self-optimizing, and self-organizing surrogate models
to approximate and speed up Multiphysics simulations. We generate two
real-world tabular datasets, which we make publicly available, and show that
surrogate models can be trained on relatively small amounts of data to
approximate the underlying simulations accurately. We conduct extensive
experiments combining four machine learning and deep learning algorithms with
two optimization algorithms and a comprehensive evaluation strategy. Finally,
we evaluate the performance of our combined training and optimization pipeline
by verifying the generated Pareto-optimal results using the ground truth
simulations. We also employ explainable AI techniques to analyse our surrogates
and conduct a preselection strategy to determine the most relevant features in
our real-world examples. This approach lets us understand the underlying
problem and identify critical partial dependencies.

这篇论文介绍了一种用于训练、自我优化和自我组织的代理模型方法论框架，用于逼近和加速多物理模拟，通过与两种优化算法和一种综合评估策略的结合训练和优化管道的性能评估，实验证明了代理模型能够准确逼近基础模拟，并使用可解释的 AI 技术分析代理模型并进行预选择策略以确定真实示例中最相关的特征。