AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems

• URL: http://arxiv.org/abs/2511.22522v1
• Date: Thu, 27 Nov 2025 15:02:53 GMT
• Title: AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems
• Authors: Hyun-Sik Jeong, Hanse Kim, Keun-Young Kim, Gaya Yun, Hyeonwoo Yu, Kwan Yun,
• Abstract summary: We introduce holographic inverse problems and demonstrate how PIML can reconstruct bulk spacetime.<n>We explicitly show how such holographic problems can be analogized to inverse problems in classical mechanics.<n>We also explore Kolmogorov-Arnold Networks (KANs) as an alternative to traditional neural networks.
• Score: 3.324986723090369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We apply physics-informed machine learning (PIML) to solve inverse problems in holography and classical mechanics, focusing on neural ordinary differential equations (Neural ODEs) and physics-informed neural networks (PINNs) for solving non-linear differential equations of motion. First, we introduce holographic inverse problems and demonstrate how PIML can reconstruct bulk spacetime and effective potentials from boundary quantum data. To illustrate this, two case studies are explored: the QCD equation of state in holographic QCD and $T$-linear resistivity in holographic strange metals. Additionally, we explicitly show how such holographic problems can be analogized to inverse problems in classical mechanics, modeling frictional forces with neural networks. We also explore Kolmogorov-Arnold Networks (KANs) as an alternative to traditional neural networks, offering more efficient solutions in certain cases. This manuscript aim to provide a systematic framework for using neural networks in inverse problems, serving as a comprehensive reference for researchers in machine learning for high-energy physics, with methodologies that also have broader applications in mathematics, engineering, and the natural sciences.

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