Author

P. Perdikaris

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#machine learning Preprint Oct 2026

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...

Juan Molina, P. Perdikaris, Mircea Petrache et al. · 0 citations

#machine learning Preprint Sep 2026

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...

S. Mao, A. Isella, P. Perdikaris et al. · 0 citations

Preprint Sep 2026

Physics-Informed Learning of Feedback-Linearizing Representations

This work proposes a cascaded physics-informed neural network (PINN) framework to approximately solve partial differential equations (PDEs) and demonstrates that this approach can computationally discover effective feedback-linearizing representations of nonlinear systems for control tasks.

Pavlos Kallinikidis, Feng-Jun Yang, David Snyder et al. · 0 citations

#machine learning Preprint Sep 2026

Latent-MoE: Domain-Aware Mixture-of-Experts for PDEs with Multi-Regime Physics

Physics-informed neural networks (PINNs) struggle on PDEs whose governing physics varies across the domain. We trace this to a structural property of standard coordinate networks: their neural tangent kernel (NTK) is translation-variant and lets training points of large coordinate magnitude disproportionately influence...

Hanwen Wang, P. Perdikaris · 0 citations