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optimization algorithms

topic2 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
papersTODAY 04:00 UTC

Benchmarking Optimizers for Inverse Problems with Differentiable Physics Simulators

A new arXiv preprint examines how different optimization algorithms perform when used to solve inverse problems inside differentiable physics simulators. The work argues that such simulators combine the physical fidelity of numerical solvers with gradient-based learning, which could benefit scientific discovery and engineering design. The study benchmarks optimizer choices to identify which ones work best in this setting.

research/benchmark/differentiable-physics/optimization