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Arrests, raids, beaten and bloodied suspects — how Russia-Azerbaijan relations have unravelled: https://benborges.xyz/2025/07/02/arrests-raids-beaten-and-bloodied.html

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo
https://arxiv.org/abs/2506.01891

Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Neural Quantum States (NQS) are a class of variational wave functions parametrized by neural networks (NNs) to study quantum many-body systems. In this work, we propose SineKAN, the NQS ansatz based on Kolmogorov-Arnold Networks (KANs), to represent quantum mechanical wave functions as nested univariate functions. We show that \sk wavefunction with learnable sinusoidal activation functions can capture the ground state energies, fidelities and various correlation functions of the 1D Transverse-F…

A #ClimateChange increase in a ticks range creates ever more people allergic to #meat and #dairy, which reduces demand for the products of an industry that aggravates the

The Phases of Chaos
Tarek Anous, Diego M. Hofman
https://arxiv.org/abs/2506.20542 https://arxiv.org/pdf/2506.20542

The Phases of Chaos
We develop a novel physical picture to understand certain universal properties of the GUE matrix model which are typically ascribed to quantum chaos, i.e. the ramp and the plateau. We argue that these features should instead be associated with a pattern of spontaneous (or weak explicit) symmetry breaking. In this language, the GUE matrix model corresponds to an effective theory that describes the symmetry-broken phase, and where the Hermitian matrix of the GUE should be understood as a massive …

Replaced article(s) found for quant-ph. https://arxiv.org/list/quant-ph/new
[2/2]:
- Probing Quantum Spin Systems with Kolmogorov-Arnold Neural Network Quantum States
Mahmud Ashraf Shamim, Eric A F Reinhardt, Talal Ahmed Chowdhury, Sergei Gleyzer, Paulo T Araujo

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Shahab Azarfar, Joseph B. Choi, Phong CH. Nguyen, Yen T. Nguyen, Pradeep Seshadri, H. S. Udaykumar, Stephen Baek
https://arxiv.org/abs/2506.12996

Latent Representation Learning of Multi-scale Thermophysics: Application to Dynamics in Shocked Porous Energetic Material
Coupling of physics across length and time scales plays an important role in the response of microstructured materials to external loads. In a multi-scale framework, unresolved (subgrid) meso-scale dynamics is upscaled to the homogenized (macro-scale) representation of the heterogeneous material through closure models. Deep learning models trained using meso-scale simulation data are now a popular route to assimilate such closure laws. However, meso-scale simulations are computationally taxing,…