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Training physics-informed neural networks with a unified priority framework can dramatically improve convergence and accuracy by respecting the physical information flow.
AI coding agents excel at translating scientific tasks into familiar formats but struggle to achieve true scientific discovery, with only 17.8% surpassing state-of-the-art benchmarks.
