EvoSim: Learning to Model, Modeling to Learn

EvoSim is a self‑evolving AI scientist that autonomously refines physics‑based models by using experimental discrepancies to revise mechanisms and equations, and by testing plausibility against held‑out data. It records exploration traces to update its knowledge, skills, and multi‑agent coordination, thereby improving both the models and its own modeling decisions. In two industrial battery tasks, EvoSim achieved a 1.79 % mean absolute error in predicting lithium‑metal‑plating onset and a 7.62 mV root‑mean‑square error in dynamic voltage prediction, outperforming human‑developed models and reducing physics errors by about 36 %.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

• agents