Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting

View PDF HTML (experimental) Abstract:Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive datasets. Petascale climate models generated in NASA laboratories require a dedicated group of graphics and media experts and access to high-performance computing resources. Scientists may need to share scientific results with the community iteratively and quickly. However, the time-consuming trial-and-error process incurs significant data transfer overhead and far exceeds the time and resources allocated for typical post-analysis visualization tasks, disrupting the production workflow. Our paper introduces a user-friendly framework for creating 3D animations of petascale, time-varying data on a commodity workstation. Our contributions: (i) Generalized Animation Descriptor (GAD) with a keyframe-based adaptable abstraction for animation, (ii) efficient data access from cloud-hosted repositories to reduce data management overhead, (iii) tailored rendering system, and (iv) an LLM-assisted conversational interface as a scripting module to allow domain scientists with no visualization expertise to create animations of their region of interest. We demonstrate the framework's effectiveness with two case studies: first, by generating animations in which sampling criteria are specified based on prior knowledge, and second, by generating AI-assisted animations in which sampling parameters are derived from natural-language user prompts. In all cases, we use large-scale NASA climate-oceanographic datasets that exceed 1PB in size yet achieve a fast turnaround time of 1 minute to 2 hours. Users can generate a rough draft of the animation within minutes, then seamlessly incorporate as much high-resolution data as needed for the final version. Comments: ©2026 IEEE. Personal use of this material is permitted. 2026 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses. N.B. Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract here is shorter than that in the original PDF file Subjects: Artificial Intelligence (cs.AI); Systems and Control (eess.SY) Cite as: arXiv:2603.07053 [cs.AI] (or arXiv:2603.07053v2 [cs.AI] for this version) https://doi.org/10.48550/arXiv.2603.07053 arXiv-issued DOI via DataCite Submission history From: Ishrat Jahan Eliza [view email] [v1] Sat, 7 Mar 2026 06:19:26 UTC (13,250 KB) [v2] Tue, 14 Apr 2026 22:15:28 UTC (13,252 KB)