AIBullisharXiv – CS AI · May 297/10
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Battery-Sim-Agent: Leveraging LLM-Agent for Inverse Battery Parameter Estimation
Researchers introduce Battery-Sim-Agent, an LLM-based framework that uses AI agents to estimate battery parameters by mimicking scientific reasoning rather than traditional black-box optimization. The system outperforms conventional methods like Bayesian optimization on benchmark tests and demonstrates practical applicability on real-world battery datasets, representing a novel approach to accelerating battery innovation through physics-informed AI reasoning.