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Georgia Institute of Technology
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PhysCoRe outperforms traditional methods by accurately predicting deformable object dynamics while adapting to new materials and guiding exploration through confidence-based feedback.
PINNs can be made dramatically more robust to noisy data (up to 96.6% error reduction) by selectively pruning neurons that are overly sensitive to corrupted samples.
