Title: Physics-informed neural network solves minimal surfaces in curved spacetime
Speaker: Norihiro Tanahashi
Abstract: We develop a flexible framework based on physics-informed neural networks (PINNs) to solve boundary value problems for minimal surfaces in curved spacetimes, with particular emphasis on singularities and moving boundaries. By encoding the underlying physical laws into the loss function and designing network architectures that incorporate singular behavior and dynamic boundaries, our approach enables robust, accurate solutions to both ordinary and partial differential equations with complex boundary conditions. We demonstrate the versatility of this framework by applying it to minimal surface problems in anti-de Sitter (AdS) spacetime, including examples relevant to the AdS/CFT correspondence (e.g., Wilson loops and gluon scattering amplitudes).
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https://kias-re-kr.zoom.us/j/82231139096?pwd=x1rbJyCEBNU3QzP7DPn9VdzbpcOafI.1
Meeting ID: 822 3113 9096
Passcode: 178946