1147 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.
The paper introduces a "Time-dependent Neural Galerkin Method" that computes entire quantum state trajectories over a finite time window by minimizing a global-in-time loss function, enabling the efficient simulation of long-time dynamics in strongly interacting systems like the Transverse-Field Ising model.
This paper introduces a generalized spin-polarized energy density method derived from spin-density functional theory that decomposes total energy into uniquely defined atomic energies, providing a new tool for analyzing magnetic systems through numerical implementations in the VASP code.
This paper demonstrates that an insect-inspired modular RL architecture, which decomposes control into specialized interacting circuits rather than a single centralized controller, outperforms monolithic MLP and GRU models in complex navigation tasks involving competing behavioral objectives.
This paper proposes a modular AI framework that utilizes a physics-based dataset of 20,000 propeller geometries to enable rapid, direct performance-to-design generation and optimization through conditional generative models and neural-network surrogates.
This paper provides compact, numerically robust, closed-form expressions and higher-order approximations for the differential and higher-order derivatives of the tangent operator on $SE(3)$, avoiding block partitioning to facilitate efficient computation in multibody systems and Cosserat continua simulations.
This paper presents a machine-learning-enhanced workflow to demonstrate how lattice dynamics and structural relaxation deepen moiré potentials in large-scale twisted supercells, thereby facilitating robust Wigner crystallization and emergent correlated electronic states.
This paper uses first-principles calculations to systematically investigate the properties of intrinsic and extrinsic defects in bulk hexagonal diamond, identifying key dopants for conductivity engineering and potential defect complexes for quantum applications.
The paper proposes a method to achieve near-deterministic loading of optical tweezer arrays by using repulsive barrier potentials to protect trapped particles during multiple loading cycles, significantly increasing filling efficiency for both atoms and molecules.
This molecular dynamics study demonstrates that while higher implantation temperatures preserve crystallinity, lower temperatures (around 500 K) actually promote better Al dopant activation by preventing the formation of large, Al-trapping interstitial clusters that emerge at higher temperatures and doses.
LARA-HPC is a validation-driven agentic framework designed to automate reliable atomistic modeling workflows on high-performance computing systems by prioritizing simulation-native verification and iterative refinement over simple code generation.