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 directly from boundary fermionic spectral functions.
By Koji Hashimoto, Hyun-Sik Jeong, Keun-Young Kim, Daichi Takeda, Kwan Yun
arXiv:2601.22033v2 Announce Type: replace
Abstract: Holography, in the form of the anti-de Sitter/conformal field theory (AdS/CFT) correspondence, offers a natural setting for generative modelling. D...
By Ehsan Mirafzali, Sanjit Shashi, Sanya Murdeshwar, Edgar Shaghoulian, Daniele Venturi, Razvan Marinescu
arXiv:2603.27936v3 Announce Type: replace-cross
Abstract: Nonlinear Partial Differential Equations (PDEs) are ubiquitous in mathematical physics and engineering. Although Physics-Informed Neural Netw...
By Sean Disar\`o, Ruma Rani Maity, Aras Bacho
arXiv:2607. 07127v1 Announce Type: cross Abstract: Lattice field theory is the workhorse of non-perturbative physics, used to simulate phenomena from the strong nuclear force to critical phenomena in materials.
By Tobias G\"obel, Julian R. Ebelt, Zier Mensch, Mathis Gerdes, Miranda C. N. Cheng
arXiv:2606. 01110v1 Announce Type: cross Abstract: Full waveform inversion (FWI) reconstructs heterogeneous material properties from receiver data but remains computationally demanding.
By Hoang Anh Nguyen, Divakar Vashisth, Ali Tura
arXiv:2606. 19303v1 Announce Type: new Abstract: High-fidelity simulation of spatiotemporal dynamics is computationally prohibitive, necessitating efficient super-resolution techniques to reconstruct high-resolution data from coarse-grained inputs.
By Xizhuo (Cici), Zhang, Zekai Wang, Fei Liu, Bing Yao