102 citations · 105 across the 3 of their papers we have counts for
3 papers
math.NA2024★ 102 cited
Weak baselines and reporting biases lead to overoptimism in machine learning for fluid-related partial differential equations
Nick McGreivy, Ammar Hakim
One of the most promising applications of machine learning (ML) in computational physics is to accelerate the solution of partial differential equations (PDEs). The key objective o…
math.NA2023★ 2 cited
Invariant preservation in machine learned PDE solvers via error correction
Nick McGreivy, Ammar Hakim
Machine learned partial differential equation (PDE) solvers trade the reliability of standard numerical methods for potential gains in accuracy and/or speed. The only way for a sol…
cs.CV2022★ 1 cited
Convolutional layers are equivariant to discrete shifts but not continuous translations
Nick McGreivy, Ammar Hakim
The purpose of this short and simple note is to clarify a common misconception about convolutional neural networks (CNNs). CNNs are made up of convolutional layers which are shift…