PhysicsNeMo functionals follow the torch.nn.functional pattern: stateless
operations designed for direct use in model code, training loops, and
pre/post-processing pipelines. They are intended to be easy to compose and to
behave consistently across CPU and GPU execution paths.
Many functionals are optimized for NVIDIA GPUs and can dispatch to accelerated implementations when those backends are installed. For operations with multiple implementations, PhysicsNeMo selects a preferred implementation by default and falls back to another supported one when needed, emitting a warning so behavior is explicit. Functionals with multiple implementations have plots available in the documentation for performance comparisons.
.. toctree:: :maxdepth: 2 :caption: PhysicsNeMo Functionals :name: PhysicsNeMo Functionals nn/functionals/neighbors nn/functionals/derivatives nn/functionals/geometry nn/functionals/fourier_spectral nn/functionals/regularization_parameterization nn/functionals/interpolation nn/functionals/neighborhood_attention