User study with 20 novices using ChatGPT identifies recurring AI visualization errors, user prompting issues, trust factors, and collaboration patterns, with distinct failure modes observed on Gemini and Claude.
Evolutionary Optimization of Physics-Informed Neural Networks: Evo-PINN Frontiers and Opportunities
6 Pith papers cite this work. Polarity classification is still indexing.
6
Pith papers citing it
years
2026 6representative citing papers
FLASH-MAX embeds exact Maxwell solutions as neurons in a neural network to reconstruct homogeneous EM fields from sparse data with guaranteed zero PDE residual and proven universal approximation on arbitrary domains.
SASAV introduces the first fully autonomous multi-agent system for scientific data analysis and visualization that operates without external prompting or human-in-the-loop feedback.
SciVisAgentSkills provides reusable agent skills that raise mean task scores on a 108-task SciVis benchmark when paired with Codex and Claude Code agents.
Empirical comparison of domain-specific, computer-use, and general-purpose LLM agents plus CLI/GUI modalities on SciVis tasks reveals general-purpose agents highest in success rate but costliest, domain-specific agents more efficient, and persistent memory beneficial depending on mode.
Context-mediated domain adaptation treats user modifications to AI artifacts as implicit domain specifications that reshape LLM-powered multi-agent reasoning, demonstrated via the Seedentia system which extracted 46 domain knowledge entries from expert edits.
citing papers explorer
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Vibe Visualizing: How Visualization Novices Try (and Fail) to Generate and Interpret Visualizations with Conversational AI
User study with 20 novices using ChatGPT identifies recurring AI visualization errors, user prompting issues, trust factors, and collaboration patterns, with distinct failure modes observed on Gemini and Claude.