Scalable physical source-to-field inference with hypernetworks
<|reference_start|>Scalable physical source-to-field inference with hypernetworks: We present a generative model that amortises computation for the field around e.g. gravitational or magnetic sources. Exact numerical calculation has either computational complexity $\mathcal{O}(M\times{}N)$ in the number of sources and field evaluation points, or requires a fixed evaluation grid to exploit fast Fourier transforms. Using an architecture where a hypernetwork produces an implicit representation of the field around a source collection, our model instead performs as $\mathcal{O}(M + N)$, achieves accuracy of $\sim\!4\%-6\%$, and allows evaluation at arbitrary locations for arbitrary numbers of sources, greatly increasing the speed of e.g. physics simulations. We also examine a model relating to the physical properties of the output field and develop two-dimensional examples to demonstrate its application. The code for these models and experiments is available at https://github.com/cmt-dtu-energy/hypermagnetics.<|reference_end|>
@article{james2024scalable,
    title={Scalable physical source-to-field inference with hypernetworks},
    author={Berian James and Stefan Pollok and Ignacio Peis and Jes Frellsen and
  Rasmus Bj{o}rk},
    journal={arXiv preprint arXiv:2405.05981},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.05981},
    primaryClass={cs.LG cs.CE physics.comp-ph}
}