102 citations · 143 across the 7 of their papers we have counts for
7 papers
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…
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…
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
Tian Qin, Alex Beatson, Deniz Oktay +2
Partial differential equations (PDEs) are often computationally challenging to solve, and in many settings many related PDEs must be be solved either at every timestep or for a var…
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…
Computation of the Biot-Savart line integral with higher-order convergence using straight segments
Nick McGreivy, Caoxiang Zhu, Lee Gunderson +1
One common approach to computing the magnetic field produced by a filamentary current-carrying coil is to approximate the coil as a series of straight segments. The Biot-Savart fie…
Optimized finite-build stellarator coils using automatic differentiation
Nick McGreivy, Stuart R. Hudson, Caoxiang Zhu
A new stellarator coil design code is introduced that optimizes the position and winding pack orientation of finite-build coils. The new code, called FOCUSADD, performs gradient-ba…
Randomized Automatic Differentiation
Deniz Oktay, Nick McGreivy, Joshua Aduol +2
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to comput…