3 papers
cs.LG2026
Hybrid Least Squares/Gradient Descent Methods for MIONets
Jun Choi, Chang-Ock Lee, Minam Moon
In this paper, we propose an efficient hybrid least squares/gradient descent (LSGD) method for MIONets to accelerate training. This method generalizes the LSGD method for DeepONets…
cs.LG2025
Hybrid Least Squares/Gradient Descent Methods for DeepONets
Jun Choi, Chang-Ock Lee, Minam Moon
We propose an efficient hybrid least squares/gradient descent method to accelerate DeepONet training. Since the output of DeepONet can be viewed as linear with respect to the last…
math.NA2025
A Neumann-Neumann Acceleration with Coarse Space for Domain Decomposition of Extreme Learning Machines
Chang-Ock Lee, Byungeun Ryoo
Extreme learning machines (ELMs), which preset hidden layer parameters and solve for last layer coefficients via a least squares method, can typically solve partial differential eq…