AdS/Deep-Learning made easy II: neural network-based approaches to holography and inverse problems
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Abstract (arXiv)
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.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 20 of its 85 references that are in the map. First posted on arXiv 27 Nov 2025.
Builds on
20 of 85 references.
References · 20
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- 2022Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodologycited by 44
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- 2020Machine Learning for Condensed Matter Physicscited by 22
- 2024Gravitational Duals from Equations of Statecited by 10
- 2020AdS/Deep-Learning made easy: simple examplescited by 9
- 2024Machine-learning emergent spacetime from linear response in future tabletop quantum gravity experimentscited by 5
- 2025Aspects of holographic entanglement using physics-informed-neural-networkscited by 5
- 2024Phase Diagram from Nonlinear Interaction between Superconducting Order and Density: Toward Data-Based Holographic Superconductorcited by 3
- 2024Enhanced BPINN Training Convergence in Solving General and Multi-scale Elliptic PDEs with Noisecited by 2
- 2025Learning the Inverse Ryu--Takayanagi Formula with Transformerscited by 2
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Citing papers per year: 2026: 2.
2 papers.