通过引导式插值可视化，帮助用户探索高维参数空间以找到最优解。
Parameter Space Analysis through Guided Visual Interpolations
- 基于XAI和不确定性量化引导参数插值
- 结合t-SNE与小多图展示插值路径全景
- 适合需要优化复杂系统参数的研究者
我们提出参数空间分析的引导式视觉插值方法（ParamInter），通过在初始参数与最优参数之间进行可探索的插值，实现对高维输入参数空间的分析。该方法结合小多图联动视图与t-SNE降维表示，提供插值过程的整体概览。通过可解释人工智能（XAI）提供的效应建议，引导用户从多个输出参数出发，向指定目标参数进行探索。相比已有工作，ParamInter整合了前沿的基于效应的XAI与不确定性量化（UCQ）方法，并引入从初始设置到最优设置的插值路径。此外，在降维数据上增加可解释层，通过叠加输入参数的小多图增强可视化效果。我们在实际的高炉优化问题中验证了该工具的有效性，该问题是通过建模与可视化解决的多目标优化任务。
原文摘要 · Abstract (English)
We propose Parameter Space Analysis through Guided Visual Interpolations (ParamInter), a novel tool for high-dimensional input parameter space analysis by making interpolation towards optimal parameter sets explorable using guided analytics. The interpolation is accompanied by both small multiples in linked views and utilizes t-Distributed Stochastic Neighbor Embedding (t-SNE) representations to show an interpolation overview. ParamInter uses a guided exploration loop focusing on the interpolation towards user-specified target parameters from many output parameters. The exploration process is additionally guided through eXplainable Artificial Intelligence (XAI)-based effect suggestions throughout our tool. ParamInter, compared to prior work, focuses on the integration of state-of-the art effect-based XAI and Uncertainty Quantification (UCQ) approaches for guidance, and introduces an interpolation towards the optimal solution through interpolation between the initial parameter setting and the optimal setting. We also add an interpretability layer for dimensionality-reduced data by displaying our novel interpolation towards the optimum, enhanced by small multiples of the input parameters on top. We demonstrate the direct applicability of our tool on a real-world use case for a blast furnace optimisation process, where a multi-objective problem is solved through modeling and visualisation.
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