An adaptive framework for the axisymmetric pulsar magnetosphere using physics-informed Kolmogorov-Arnold networks
arXiv:2606.10686v1 Announce Type: cross Abstract: The pulsar magnetosphere has only recently been addressed using Physics-Informed Neural Networks (PINNs), by deploying a domain-decomposition approach and treating the separatrix and equatorial current sheet as infinitesimally thin discontinuities. However, this baseline requires extensive manual hyperparameter tuning, achieves limited final accuracy and demands several hours of training. We refine this framework by introducing domain-specific neural architectures based on Kolmogorov-Arnold networks, an automated adaptive training pipeline and
The proliferation of advanced neural network architectures like Kolmogorov-Arnold networks and the increasing demand for high-fidelity scientific simulations are enabling more sophisticated approaches to complex physics problems.
This development indicates a significant advancement in the application of AI, specifically PINNs, for solving fundamental physics problems, potentially accelerating scientific discovery and engineering innovation.
The accuracy, efficiency, and robustness of AI models applied to complex physical systems are significantly improved, reducing reliance on extensive manual tuning and computational resources for intractable problems.
- · AI/ML researchers (Physics-Informed NNs)
- · Astrophysics community
- · High-performance computing sector
- · Scientific simulation software developers
- · Traditional numerical simulation methods
- · Researchers lacking access to advanced AI tools
More accurate and faster simulations of complex astrophysical phenomena become possible, leading to new insights into pulsars and magnetospheres.
The refined framework could be generalized to other complex multi-physics problems, accelerating research in diverse scientific and engineering fields.
This could lead to a 'democratization' of complex scientific modeling, enabling smaller research groups to tackle previously hardware- and expertise-intensive problems.
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Read at arXiv cs.LG