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 snapshot-referencing (SSR), a software-based retrospective drift correction method that utilizes a fast-scan reference image and flexible basis functions to eliminate spatial distortions in long-duration S(T)EM spectral mapping, thereby restoring the integrity of hyperspectral data without requiring specialized hardware.
This study utilizes the open-source TMAP8 code to integrate component-level surrogate models with system-level fuel cycle modeling, enabling a computationally efficient multiscale assessment of tritium transport and retention in Tokamak Energy Ltd.'s preliminary fusion pilot plant design to optimize safety, economics, and design iterations.
This article reviews the paradigm of data-driven scientific machine learning, specifically highlighting how neural operators offer a faster, resolution-invariant alternative to conventional numerical methods for solving partial differential equations, while also addressing their potential to complement traditional techniques and noting existing challenges.
This paper proposes an enhanced unsupervised machine learning method using a non-fully connected deep neural network to calculate the ground states of two-body systems with spin and isospin degrees of freedom, successfully validating the approach by reproducing the unique bound state of the deuteron.
The paper introduces LaSDI-IT, a data-driven framework that combines a revised auto-encoder with explicit interface-aware encoding and latent dynamics learning to efficiently and accurately model shock-induced pore collapse in high explosives, achieving high-fidelity accuracy with half the training data and a 106-fold speedup over conventional simulations.
This study demonstrates that targeted phonon excitation can reversibly modulate the thermal conductivity of bulk boron arsenide, revealing that while three-phonon interactions allow for bidirectional control, the inclusion of four-phonon scattering fundamentally shifts the effect toward significant, frequency-dependent suppression.
This paper demonstrates that a Global Annealing Monte Carlo algorithm, which integrates machine learning-proposed global moves with essential local moves, robustly outperforms state-of-the-art classical methods like Simulated Annealing and Population Annealing in solving three-dimensional Ising spin glass problems without requiring hyperparameter tuning.
This paper presents a general framework combining a lattice-Bisognano-Wichmann ansatz with multi-replica-trick quantum Monte Carlo methods to accurately reconstruct entanglement Hamiltonians in diverse two-dimensional quantum systems, demonstrating that the ansatz remains a valid approximation even in the absence of Lorentz invariance and translational symmetry.
This study demonstrates that accurately representing deep ocean features, particularly mesoscale eddies, in initial conditions is critical for improving ensemble forecast performance of surface fields in the Gulf of Mexico, thereby motivating the assimilation of deep observations to better constrain full-water-column circulation.
The paper introduces LEDDS, an open-source framework that achieves high-performance, portable LBM-DEM simulations on GPUs by expressing complex fluid-particle interactions entirely through algorithmic primitives, delivering performance comparable to hand-tuned CUDA solvers while maintaining code clarity and broad hardware compatibility.