New adaptive Adam optimizer improves deep learning convergence for PDEs

A new paper introduces a learning-rate-adaptive variant of the Adam optimizer designed to improve convergence in deep learning, particularly for solving partial differential equations. The proposed method adjusts the learning rate based on empirical estimates of the objective function, aiming to overcome limitations of standard Adam and SGD with constant learning rates. Numerical simulations demonstrate faster reductions in objective function values compared to the default Adam optimizer, and theoretical analysis provides a rigorous proof of convergence to the global minimizer for certain adaptive SGD variants. AI

IMPACT This research could lead to more stable and efficient training of deep learning models, particularly for complex scientific simulations.

RANK_REASON The cluster contains a research paper detailing a new optimization method for deep learning. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

• Adam
• Adam optimizer
• Adrian Riekert
• deep Kolmogorov methods
• deep learning
• deep Ritz methods
• partial differential equations
• physics-informed neural networks
• PyTorch
• stochastic gradient descent
• Tensorflow

• paper
• other

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