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AI reconstructs spacetime from quantum data using Neural ODEs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed a novel machine learning framework utilizing Neural Ordinary Differential Equations to reconstruct spacetime geometries from fermionic spectral functions. This physics-informed approach can accurately infer the properties of charged black holes in anti-de Sitter space, including the probe charge, across different quantum critical regimes. The study also highlights a phenomenon of isospectral non-uniqueness, where distinct bulk profiles can yield identical spectral functions, demonstrating the network's ability to capture universal infrared behavior. AI

IMPACT Introduces a novel physics-informed machine learning approach for complex scientific modeling.

RANK_REASON Academic paper detailing a new machine learning method applied to theoretical physics. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

AI reconstructs spacetime from quantum data using Neural ODEs

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Academic paper detailing a new machine learning method applied to theoretical physics. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Koji Hashimoto, Hyun-Sik Jeong, Keun-Young Kim, Daichi Takeda, Kwan Yun ·

    Deep learning emergent spacetime from fermionic spectral functions in holography

    arXiv:2609.18566v1 Announce Type: cross Abstract: We present a physics-informed machine learning framework based on Neural Ordinary Differential Equations that solves the holographic inverse problem: reconstructing the bulk spacetime and gauge field of a charged AdS black hole di…

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