Deep neural networks are framed as discrete dynamical systems, and PINNs are shown to approximate the same PDE dynamics as classical discretization but through dense parameter representations rather than structured stencils.
A survey on video large language models: Benchmarks and evaluation methodologies
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Deep Neural Networks as Discrete Dynamical Systems: Implications for Physics-Informed Learning
Deep neural networks are framed as discrete dynamical systems, and PINNs are shown to approximate the same PDE dynamics as classical discretization but through dense parameter representations rather than structured stencils.