De Rham PINNs address topological failures in physics-informed neural networks
Hodge-de Rham PINNs parameterize 1-forms to solve PDEs with topological holes and voids.
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Stories tagged #physics-informed-neural-networks
Paper proves orthogonal symmetry of the differential neural tangent kernel
Operators from the same group orbit share spectra, and shallow RePU networks get an eigenvalue decay rate.
Retrainable physics-integrated neural differentiable modeling of sintering across material systems
Sinter-PiNDiff predicts ceramic density and grain growth from sparse data across three material systems.