{"@context":"https://schema.org","@type":"CreativeWork","@id":"https://froggit.ai/public/capsules/ae5c1a1e-3fd1-40e8-b180-d37729453dcc","identifier":"ae5c1a1e-3fd1-40e8-b180-d37729453dcc","url":"https://froggit.ai/public/capsules/ae5c1a1e-3fd1-40e8-b180-d37729453dcc","name":"ANTIC: Adaptive Neural Temporal In-situ Compressor","text":"# ANTIC: Adaptive Neural Temporal In-situ Compressor\n\nSource-backed public reference for arXiv:2604.09543.\n\n**Authors:** Sandeep S. Cranganore, Andrei Bodnar, Gianluca Galletti, Fabian Paischer, Johannes Brandstetter\n**Primary source:** https://arxiv.org/abs/2604.09543\n**Published:** 2026-04-10T17:58:16Z\n**Updated:** 2026-05-13T13:54:12Z\n**Categories:** cs.LG\n\n## Abstract Summary\nThe persistent storage requirements for high-resolution, spatiotemporally evolving fields governed by large-scale and high-dimensional partial differential equations (PDEs) have reached the petabyte-to-exabyte scale. Transient simulations modeling Navier-Stokes equations, magnetohydrodynamics, plasma physics, or binary black hole mergers generate data volumes that are prohibitive for modern high-performance computing (HPC) infrastructures. To address this bottleneck, we introduce ANTIC (Adaptive Neural Temporal in situ Compressor), an end-to-end in situ compression pipeline. ANTIC consists of an adaptive temporal selector tailored to high-dimensional physics that identifies and filters informative snapshots at simulation time, combined with a spatial neural compression module based on continual fine-tuning that learns residual updates between adjacent snapshots using neural fields. By operating in a single streaming pass, ANTIC enables a combined compression of temporal and spatial components and effectively alleviates the need for explicit on-disk storage of entire time-evolved trajectories. Experimental results demonstrate how storage reductions of several orders of magnitude relate to physics accuracy.\n\n## Public Use Notes\n- arXiv metadata/abstract summary only; not independent replication or endorsement.\n- Use as a cited research reference for discovery, retrieval, and agent context.\n- For clinical, security, operational, or deployment-sensitive topics, treat as research context, not medical, legal, safety, or engineering advice.\n\n## Source\n- https://arxiv.org/abs/2604.09543\n","keywords":["cs.LG"],"about":[],"citation":["https://arxiv.org/abs/2604.09543"],"isPartOf":{"@type":"Dataset","name":"Forge Cascade Knowledge Graph","url":"https://froggit.ai"},"publisher":{"@type":"Organization","name":"Forge Cascade","url":"https://froggit.ai"},"dateCreated":"2026-04-13T06:00:03.462000Z","dateModified":"2026-06-19T13:48:06Z","isBasedOn":"https://arxiv.org/abs/2604.09543","additionalProperty":[{"@type":"PropertyValue","name":"trust_level","value":40},{"@type":"PropertyValue","name":"verification_status","value":"sources_verified"},{"@type":"PropertyValue","name":"provenance_status","value":"valid"},{"@type":"PropertyValue","name":"evidence_level","value":"primary_source"},{"@type":"PropertyValue","name":"content_hash","value":"f90e276cf0cefdbbcc30abaa0ff101d6c313d0d7795bea604bf8f6399b1d8d0d"}]}