1258 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 presents an energy-conserving contact dynamics framework for arbitrary convex nonspherical rigid-body particles in 2D and 3D that prevents overlap while enabling continuous force evaluation, thereby providing a robust foundation for modeling the structural and transport properties of complex colloidal and granular systems.
This paper introduces measurement-dressed imaginary-time evolution (MDITE) as a novel framework for studying mixed-state phase transitions driven by the competition between coherence-restoring dynamics and decoherence, demonstrating the existence of new critical behaviors in one- and two-dimensional models that fall outside known universality classes.
By combining the energy resolution limits of quantum sensors with the human brain's metabolic power, this paper establishes a technology-independent fundamental bound of approximately 2.2 Mbit/s on the information capacity of magnetoencephalography, revealing an inherent spatio-temporal trade-off that limits the spatial complexity of detectable neural patterns.
This paper introduces a gradient-based optimization method using straight-through Gumbel-Softmax estimation to enable efficient parameter inference and inverse design in exact stochastic kinetic models by approximating gradients through continuous relaxation while preserving discrete stochastic dynamics in the forward pass.
This paper introduces Topology-Aware PINNs (TAPINN), a novel framework that employs supervised metric regularization and alternating optimization to effectively resolve spectral bias and mode collapse in multi-regime physics-informed neural networks, achieving superior convergence stability and accuracy compared to standard and hypernetwork-based baselines.
This paper empirically demonstrates that while Kolmogorov-Arnold Networks (KANs) can compete with MLPs on simple univariate residuals in hard-constrained recurrent physics-informed architectures, they suffer from severe hyperparameter fragility, instability in deeper configurations, and consistent failure on multiplicative terms, ultimately revealing limitations in their additive inductive bias for modeling state coupling in oscillatory systems.
This study presents the first systematic comparison of seven hydrodynamic codes simulating the unstratified streaming instability, revealing broad qualitative agreement across methods while identifying dust modeling choices and resolution as key factors influencing quantitative density statistics and highlighting the superior energy efficiency and scalability of GPU-based implementations.
This paper compares cache-conscious sort-and-sweep and tree-code algorithms for neighborhood computation in two-dimensional discrete element method simulations, finding that while tree codes offer slightly better performance and improved parallelization potential, they come at the cost of significantly increased code complexity.
This study presents an inverse-design framework combining genetic algorithms with frequency-domain micromagnetics to successfully discover unconventional two-dimensional magnonic crystal structures featuring large band gaps, thereby addressing the challenges of optimizing complex lattice geometries for targeted spin-wave properties.
This paper introduces "Ara," an LLM-based agentic workflow that leverages chemical priors to efficiently navigate the design space of covalent organic frameworks, successfully identifying durable and active photocatalysts for solar hydrogen production with significantly higher hit rates and faster convergence than random search or Bayesian optimization.