الباحثون

Paris Perdikaris

المنشورات 2

نسخة أولية وصول مفتوح

Operator-informed initialization for Fourier features physics-informed neural networks

Physics-Informed Neural Networks (PINNs) typically exhibit spectral bias, where some frequencies of the target function converge more slowly than others. In this work, we analyze the training dynamics of Fourier Feature PINNs in the Neural Tangent Kernel regime to address this limitation. We derive an explicit evolutio …

نسخة أولية وصول مفتوح

SIFARI: Self-Supervised Interferometric Fitting for Astronomical Radio Imaging

Radio-interferometric images are reconstructed from sparsely sampled visibilities, and CLEAN-based imaging can struggle with spatial filtering, complex morphologies, and uncertainty quantification. Alternative methods that fit visibilities directly can address some of these limitations but often require manual choices …

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