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cs.LG2021
Designing Rotationally Invariant Neural Networks from PDEs and Variational Methods
Tobias Alt, Karl Schrader, Joachim Weickert +2
Partial differential equation (PDE) models and their associated variational energy formulations are often rotationally invariant by design. This ensures that a rotation of the inpu…
math.NA2021
Connections between Numerical Algorithms for PDEs and Neural Networks
Tobias Alt, Karl Schrader, Matthias Augustin +2
We investigate numerous structural connections between numerical algorithms for partial differential equations (PDEs) and neural architectures. Our goal is to transfer the rich set…