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composite loss functions

topic1 events
papersTODAY 04:00 UTC

Pullback-corrected auxiliary variable optimizer targets multi-term scientific ML losses

A new arXiv paper proposes a pullback-corrected scalar auxiliary variable (PB-SAV) optimizer that adds momentum and adaptive mobility. The method is aimed at scientific machine learning objectives that combine several loss terms, such as the residual, boundary, initial, and data losses used in physics-informed neural networks. The abstract frames the work as addressing optimization challenges specific to these composite objectives.

optimization/scientific-ml/pinns/arxiv