1259 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 proposes a unified gas-kinetic framework that classifies molecules by their collision histories over an observation timescale to bridge molecular kinetics and continuum fluid dynamics, thereby recovering both the Boltzmann and Navier-Stokes equations as limiting cases and offering a new perspective on Hilbert's sixth problem.
This paper surveys various natural and artificial dynamic systems to propose a general framework that defines computation as a mapping between a system's evolution and that of an abstract computational machine, while also addressing related challenges such as uncomputability and the quantification of computational value.
This paper presents a massively parallel GPU-based simulation framework coupled with a physics-informed machine learning surrogate to overcome multiscale modeling challenges in hybrid magnon-photon systems, enabling high-fidelity, rapid prototyping of next-generation quantum and spintronic devices.
This paper introduces the Domain Translation algorithm, which overcomes the limitations of traditional domain decomposition on Exascale systems by achieving unprecedented performance and perfect weak scaling (88% of peak) on a 64-node Cerebras CS-3 cluster to simulate planetary-scale tsunamis at 112 PFLOP/s.
This paper demonstrates that quantized tensor trains (QTT) overcome the curse of dimensionality in multi-asset option pricing by enabling full-grid solutions for the Black-Scholes PDE on up to 15 assets with high accuracy and polynomial scaling, offering a viable alternative to Monte Carlo methods.
This paper introduces a lightweight, data-free framework for updating Dynamic Mode Decomposition (DMD) surrogate models to accommodate changes in reservoir permeability and well locations, enabling rapid, high-fidelity optimization and uncertainty analysis for geological carbon storage without the computational cost of retraining.
This paper proposes a multiphysics computational model for the Molten Salt Fast Reactor using the Cardinal framework to integrate NekRS for thermal hydraulics with the Fission Matrix method for efficient neutronics, replacing the standard OpenMC solver to enable fast and accurate simulations of the tightly coupled fuel-coolant system.
This paper introduces a rigorous random matrix simulation framework for modeling polarized light transport through disordered media using extended scattering channels, which offers flexible illumination profiles, novel geometric insights into the optical memory effect, and is accompanied by a free codebase.
This paper investigates how orbital transformations affect Trotter error in Quantum Phase Estimation, finding that while general recipes to minimize this error via basis selection or dynamic changes are challenging to derive, localized orbital bases—which offer low circuit depth—do not actually produce large Trotter errors in molecular calculations.
The paper introduces Scale-PINN, a novel learning strategy that integrates the iterative residual-correction principle of numerical solvers into Physics-Informed Neural Networks to drastically reduce training time and improve accuracy, thereby bridging the gap between deep learning and traditional numerical methods for practical scientific applications.