903 papers
Computational physics bridges the gap between abstract theory and real-world observation by using powerful computers to solve complex physical problems. This field allows scientists to simulate everything from the collision of subatomic particles to the swirling dynamics of galaxies, offering insights that traditional experiments alone cannot provide.
On Gist.Science, we continuously process every new preprint in this category from arXiv to make these breakthroughs accessible to everyone. Each entry is accompanied by both a clear, plain-language explanation and a detailed technical summary, ensuring that researchers and curious readers alike can grasp the significance of the latest findings without getting lost in dense equations.
Below are the latest papers in computational physics, curated to keep you at the forefront of this rapidly evolving discipline.
Using neural network variational Monte Carlo methods, this study maps the zero-temperature phase diagram of a 2D spin-imbalanced Fermi gas, revealing a rich landscape of exotic states including the Fulde-Ferrell-Larkin-Ovchinnikov phase, polarized superfluids, phase separation, and a unique crystalline phase of Cooper pairs embedded in a Fermi fluid.
This paper proposes a novel method for solving the time-dependent Schrödinger equation by modeling Bohmian trajectories as a self-consistent normalizing flow, where a neural network learns the score function to recover quantum dynamics for nodeless wave functions.
This paper introduces "joint dynamic programming," a co-design framework that simultaneously optimizes continuous hardware geometry and adaptive measurement policies to significantly outperform traditional non-adaptive or separately optimized approaches in sensing tasks, as demonstrated by substantial error reductions across radar, quantum, and photonic sensor case studies.
This paper presents the first quantum-hardware implementation of a Schrödingerisation-based algorithm for simulating time-domain Maxwell's equations, demonstrating accurate retrieval of electromagnetic field amplitudes and directions on an IonQ QPU for both benchmark problems and scattered fields.
This study demonstrates that Born-Oppenheimer molecular dynamics based on density functional-based tight-binding accurately captures low-frequency spectral density features arising from both slow intramolecular vibrations and protein fluctuations in bacteriochlorophyll molecules, outperforming classical force fields and normal mode analysis across various light-harvesting complexes.
This paper reviews fundamental linear algebra routines and computational methods, with a specific focus on eigenvalue problems and matrix decompositions essential for solving quantum systems, while highlighting the efficiency of modern freely available libraries.
This paper employs numerical simulations of coupled scalar fields with opposite kinetic terms to demonstrate that ghost-induced instabilities in classical field theories are not instantaneous but are instead mediated by nonlinear spectral energy transfer, allowing for long-lived metastable regimes controlled by spectral content, amplitude, and specific self-interaction potentials.
This paper proposes a universal spin-axis-layer locking (SALL) paradigm, demonstrated via first-principles calculations on twisted bilayer CuBr2, which enables intrinsic bipolar altermagnetic semiconductors with gate-tunable, simultaneous switching of carrier type, spin, and active layer without requiring external strain.
This paper demonstrates that tensor networks offer a robust and scalable solution for Synthetic Aperture Radar (SAR) object classification, effectively balancing high accuracy under noisy and poisoned data conditions with the model efficiency required for edge device deployment.
The paper argues that the Navier-Stokes equations face a logical paradox because numerical noise—driven by the choice of time-step—can fundamentally alter the resulting flow statistics, suggesting that small disturbances cannot be neglected in turbulence modeling.