DeepOPiraKAN learns parameter-to-spectrum mappings via operator learning and achieves relative errors of O(10^{-6}) to O(10^{-4}) for Kerr black hole quasinormal modes up to n=7 when benchmarked against Leaver's method.
Solving the Regge-Wheeler and Teukolsky equations: supervised versus unsupervised physics-informed neural networks,
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Admissible negative-w_r branches of trace-quadratic f(R,T) black holes support axial ringdown spectra governed by a single master equation equivalent to Einstein gravity plus frozen anisotropic fluid, differing from Schwarzschild by ~22% with no resolved α dependence.
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.
citing papers explorer
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Physics informed operator learning of parameter dependent spectra
DeepOPiraKAN learns parameter-to-spectrum mappings via operator learning and achieves relative errors of O(10^{-6}) to O(10^{-4}) for Kerr black hole quasinormal modes up to n=7 when benchmarked against Leaver's method.
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Odd-parity perturbations of trace-quadratic $f(R,T)$ black holes with anisotropic matter: admissible branches, axial ringdown, and a coupled-PINN benchmark
Admissible negative-w_r branches of trace-quadratic f(R,T) black holes support axial ringdown spectra governed by a single master equation equivalent to Einstein gravity plus frozen anisotropic fluid, differing from Schwarzschild by ~22% with no resolved α dependence.
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Beyond Data-Driven: How Physics-Informed Neural Networks are Reshaping Multi-Physics Design and Discovery
A review assessing PINN advances for forward modeling, inverse design, and equation discovery across multi-physics domains.