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Found 4 papers, 1 papers with code
Invariant preservation in machine learned PDE solvers via error correction
Though this strategy is different from how standard solvers preserve invariants, it is necessary to retain the flexibility that allows machine learned solvers to be accurate at large $\Delta x$ and/or $\Delta t$.
Meta-PDE: Learning to Solve PDEs Quickly Without a Mesh
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 variety of candidate boundary conditions, parameters, or geometric domains.
Convolutional layers are equivariant to discrete shifts but not continuous translations
This is because shift equivariance is a discrete symmetry while translation equivariance is a continuous symmetry.
Randomized Automatic Differentiation
The successes of deep learning, variational inference, and many other fields have been aided by specialized implementations of reverse-mode automatic differentiation (AD) to compute gradients of mega-dimensional objectives.
