1172 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 two hard-constrained Physics-informed Neural Network (PINN) formulations—the windowing and buffer approaches—that embed interface physics directly into the solution representation to overcome the accuracy limitations of soft-constraint methods, with the buffer approach demonstrating superior robustness for complex two-dimensional interface problems.
This paper introduces "Integral Decimation," a quantum-inspired algorithm that decomposes multidimensional integrals into a spectral tensor train representation to overcome the curse of dimensionality, enabling efficient and accurate calculations of free energy, entropy, and quantum dynamics in high-dimensional systems where conventional methods fail.
This paper demonstrates that athermal disordered sphere packings subjected to cyclic inverse design evolve toward a marginally absorbing manifold that encodes memory of the training range through return-point memory, driven by gradient discontinuities in the trained quantities.
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.