1189 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 hybrid classical-quantum framework for image classification that compresses classical neural network bottleneck layers into matrix product operators (MPOs), further disentangles them using two novel algorithms, and deploys the resulting circuits on quantum hardware while keeping the rest of the network on classical devices.
This study presents a one-dimensional multifluid hydrodynamic simulation of lead and steel plate collisions in explosive welding using a Godunov-type algorithm with pressure relaxation and the Volume-of-Fluid method, successfully capturing tensile stresses and validating the unloading wave arrival time against experimental data.
This paper introduces an Asymptotic-Preserving Neural Network framework that embeds the governing physics of a multiscale viscoelastic blood flow model to reliably identify arterial wall parameters and reconstruct pressure waveforms using non-invasive Doppler ultrasound data.
The paper introduces \textsc{Dynamite}, a high-performance, reproducible framework that enables the accurate solution of Dynamical Mean-Field Equations up to unprecedented times () by combining adaptive time stepping, non-uniform interpolation, and memory renormalization to overcome previous numerical limitations in studying slow dynamics in complex systems.
This paper demonstrates that using Ensemble Kalman Inversion to systematically calibrate the parameters of a neural network mesoscale eddy parameterization significantly reduces mean state and variability biases in coarse-resolution global ocean models, offering a practical pathway to improve their accuracy without requiring integration to statistical equilibrium.
This study employs numerical simulations to demonstrate that shear flow induces distinct conformational changes, tumbling dynamics, and rheological behaviors—including negative viscosity—in two-dimensional active polar semiflexible polymers, with activity significantly influencing their response until high shear rates restore passive-like characteristics.
This paper proposes using Deep Operator Networks (DeepONets) as a computationally efficient surrogate for the SWAN numerical wave model to accurately predict wave-induced forces and significant wave heights, thereby enabling more precise and efficient coupling with circulation models for storm surge prediction.
This paper introduces DD-03B, a massive 40 TB database containing 0.3 billion all-atom dissociation trajectories for over 19,000 ligand-protein complexes, which establishes a foundational resource for training AI models to predict drug-protein kinetics by categorizing interaction mechanisms and providing computed dissociation rates for systems lacking experimental data.
This study utilizes a 0D Monte Carlo simulation to demonstrate that suprathermal enhancement in advanced fusion fuels is limited, revealing that pure deuterium cannot sustain a chain reaction, DT requires zero neutron leakage for criticality, and aneutronic fuels like BH yield minimal energy gains dominated by neutron-driven processes rather than alpha-particle avalanches.
This paper presents the implementation of ab initio Hubbard parameter calculation schemes, including a novel linear-response method for energy-dependent parameters, within CP2K's k-point sampling real-time TDDFT program, highlighting the distinct theoretical advantages and dynamical applications of this approach compared to the ACBN0 scheme.