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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