1164 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.
This paper introduces AMBer, an autonomous reinforcement learning framework that efficiently constructs viable neutrino flavor theories by systematically selecting symmetry groups and particle representations while minimizing free parameters, demonstrating its potential to automate complex theoretical model-building tasks.
This paper introduces a rigorous Bayesian framework for MINFLUX microscopy that optimizes scanning patterns to achieve maximal resolution with minimal photons or exposure, potentially reducing the photon count required for 1 nm resolution by a factor of four compared to current heuristic methods.
This paper introduces an open-shell frozen natural orbital approach based on ZAPT2 that significantly reduces the virtual orbital space for quantum eigensolvers while maintaining high accuracy, enabling efficient and precise simulation of singlet-triplet energy gaps in large open-shell systems like the Ir(ppy) complex.
This paper proposes an iterative learning framework that combines evolutionary algorithms, atomic foundation models, and the stochastic self-consistent harmonic approximation to enable efficient and accurate crystal structure prediction for highly anharmonic materials by drastically reducing training data requirements while leveraging statistical averaging to mitigate potential errors.
This paper extends the Chebyshev Accelerated Subspace iteration Eigensolver (ChASE) to efficiently compute thousands of the smallest positive eigenpairs of pseudo-hermitian Hamiltonians for excitonic materials by introducing an oblique Rayleigh-Ritz projection with quadratic convergence and a parallel implementation of the Chebyshev filter optimized for exascale systems.
This paper introduces a five-level expert-curated evaluation framework to demonstrate that while large language models excel at explicit derivations in quantum field theory and string theory, they systematically fail when tasks require reconstructing tacit reasoning or maintaining global conceptual consistency, thereby revealing the limitations of current evaluation paradigms for highly abstract theoretical physics.
SWEEP is a unified, extensible, and differentiable wave equation solver framework that supports diverse seismic propagation models and plug-and-play components to facilitate advanced gradient-based inversion tasks like full-waveform inversion and least-squares reverse time migration.
This paper introduces an LSTM-PINN framework that leverages depth-recursive memory mechanisms to overcome numerical stiffness and gradient disparities in strongly coupled steady-state electrothermal systems, thereby achieving superior accuracy and multi-field consistency compared to state-of-the-art baselines across diverse convective and drag regimes.
This paper presents a machine learning framework that combines tailored convolutional neural networks with a Bregman distance-enhanced generative diffusion model to classify and augment structured light propagation data in turbulent atmospheres, effectively addressing challenges related to limited datasets and high-frequency mode generation.
This paper proposes and validates a hybrid Physics-Informed Neural Network (PINN) framework that employs an auxiliary finite-difference term to regularize the gradients of the PDE residual field, demonstrating that this approach significantly improves the accuracy of specific physical quantities of interest, such as outer-wall flux and boundary conditions, in both 2D and 3D heat-conduction benchmarks without replacing the primary automatic-differentiation-based residual.