Parameter Space Analysis through Guided Visual Interpolations

ParamInter enables non-experts to understand complex optimization decisions: The tool uses visual interpolation and guided exploration to make high-dimensional parameter adjustments interpretable, demonstrated through a real-world blast furnace optimization case where multiple competing objectives needed to be balanced.

XAI integration provides transparency into why parameter changes matter: By incorporating explainable AI (XAI) and uncertainty quantification approaches, ParamInter suggests which parameter effects are most important, helping stakeholders understand the reasoning behind optimization recommendations rather than treating them as black boxes.

Interactive visualization helps users explore trade-offs between different solutions: The tool's use of small multiples and t-SNE visualizations allows users to see how moving from current to optimal parameter settings affects outcomes, supporting more informed decision-making in complex industrial processes.

Uncertainty quantification is built into the guidance process: By integrating uncertainty quantification alongside effect-based explanations, ParamInter acknowledges that optimization recommendations come with varying levels of confidence, promoting more responsible deployment of AI-driven suggestions in real-world applications.

Interpretability is maintained even after dimensionality reduction: The novel approach of displaying interpolation paths with input parameter details on top of reduced-dimension visualizations helps preserve explainability—a key concern when using techniques like t-SNE that can obscure original data relationships.