ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging

TL;DR

ParamsDrag offers an interactive, visualization-based approach for exploring and fine-tuning simulation parameters efficiently, reducing reliance on extensive numerical simulations.

Contribution

It introduces a novel model enabling intuitive parameter adjustment through direct visualization dragging, inspired by DragGAN, enhancing efficiency in scientific simulation tuning.

Findings

Effective in real-world simulations

Outperforms existing deep learning methods

Enables intuitive parameter control

Abstract

Numerical simulation serves as a cornerstone in scientific modeling, yet the process of fine-tuning simulation parameters poses significant challenges. Conventionally, parameter adjustment relies on extensive numerical simulations, data analysis, and expert insights, resulting in substantial computational costs and low efficiency. The emergence of deep learning in recent years has provided promising avenues for more efficient exploration of parameter spaces. However, existing approaches often lack intuitive methods for precise parameter adjustment and optimization. To tackle these challenges, we introduce ParamsDrag, a model that facilitates parameter space exploration through direct interaction with visualizations. Inspired by DragGAN, our ParamsDrag model operates in three steps. First, the generative component of ParamsDrag generates visualizations based on the input simulation parameters. Second, by directly dragging structure-related features in the visualizations, users can intuitively understand the controlling effect of different parameters. Third, with the understanding from the earlier step, users can steer ParamsDrag to produce dynamic visual outcomes. Through experiments conducted on real-world simulations and comparisons with state-of-the-art deep learning-based approaches, we demonstrate the efficacy of our solution.