arXiv:2606.11518v1 Announce Type: new Abstract: Fourier neural operators (FNOs) are effective and efficient surrogates for approximating solutions of PDEs and generalize across discretizations. However, owing to the reliance on frequency truncation to maintain learning efficiency of FNOs, empirical studies suggest that FNOs exhibit spectral bias toward low-frequency information, which may hinder the learning capability especially for certain PDEs with strong high-frequency oscillations. To address this limitation, we propose SirenFNO, a novel framework that leverages sinusoidal representation
The continuous drive for more efficient and accurate AI models for scientific computing is pushing the boundaries of existing neural operator architectures.
Improving the accuracy and efficiency of Fourier Neural Operators for complex simulations could accelerate research and development in various scientific and engineering fields.
The ability to accurately model high-frequency oscillations in PDEs with technologies like SirenFNO could lead to more reliable and generalizable AI surrogates for complex physical systems.
- · Scientific Computing
- · AI/ML Researchers
- · Engineering Simulation Software Developers
- · Material Science
- · Traditional Numerical Solvers (in some applications)
More accurate and faster simulations across physics, chemistry, and engineering.
Reduced experimental costs and accelerated discovery cycles in domains relying on complex simulations.
New classes of materials or designs made possible by previously intractable simulation capabilities.
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Read at arXiv cs.LG