Author

Siddhartha Mishra

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Open access Sep 2026

Parametric phase-field brittle fracture computations with convolutional Fourier neural operators

The variational phase-field approach to fracture has emerged as a powerful framework for modeling complex fracture phenomena, yet in the computational setting its reliance on discretizations that resolve small length scales makes it very expensive for parametric studies and real-time applications. In this paper, we p...

Aryan Sinha, M. Manav, Bogdan Raonic et al. · Computational Mechanics · 0 citations

#machine learning Preprint Sep 2026

Preconditioned Physics-Informed Neural Operator Training

Neural operators are typically trained in a supervised fashion, which requires a dataset to be generated with a classical solver. Training them physics-informed, i.e., purely from the governing equations, removes this large offline cost and allows fresh samples to be drawn at every optimization step, but has so far bee...

Shi-Zheng Wen, Siddhartha Mishra, Marius Zeinhofer · 0 citations

#machine learning Preprint Sep 2026

Does Transolver really need a Transformer?

The widely used Transolver family of neural operators is based on physics-attention, which softly assigns the points of an unstructured mesh to a small number of slices, applies self-attention among the resulting tokens, and broadcasts the result back to the points. We provide a comprehensive empirical and theoretical...

Shi-Zheng Wen, Siddhartha Mishra · 0 citations