903 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 presents a hybrid Volume-of-Fluid Phase-Field method with adaptive mesh refinement for direct numerical simulations of soluble surfactant-laden flows, which accurately captures the coupling between bulk and interfacial transport to demonstrate how Marangoni stresses significantly alter bubble rise dynamics in three-dimensional geometries.
This paper presents a reinforcement learning approach combined with neural network surrogate models to optimize multigroup energy structures for one-dimensional spherical k-criticality neutron transport problems, achieving accuracy comparable to or better than existing methods while offering greater flexibility and computational efficiency.
This paper introduces a filter-assisted sample-based quantum diagonalization protocol that engineers wavefunction sparsity via a tensor-network-optimized quantum filter to overcome the sampling efficiency limitations of existing methods, thereby significantly reducing energy estimation errors and sampling overhead for strongly correlated systems.
This paper introduces hybrid neural world models, a single-network framework that predicts physical dynamics with significant speedups over classical solvers while implicitly generating an error map to detect sharp discontinuities like shocks and contacts, enabling a fallback mechanism that substantially reduces prediction errors without requiring additional calibration or governing-equation knowledge.
This paper demonstrates that under high-enthalpy Mars-entry conditions, freestream disturbances can trigger a three-step instability mechanism within the detached bow shock and shear-entropy layer, leading to nonlinear breakdown and significantly enhanced wall heating that explains flight data from Mars missions without requiring classical boundary-layer transition.
This study demonstrates that a system-specific MACE potential trained on spin-polarized DFT data for disordered Fe-Ni structures significantly outperforms existing foundation models in predicting structural, elastic, and finite-temperature properties, though it still struggles to accurately capture magnetic collapse effects governing the bcc-to-hcp phase transition.
This paper presents a Wigner-Eckart factorization of the spectral Boltzmann collision operator that reduces the problem's dimensionality from eight to five by aligning the frame with colliding pairs, thereby decoupling angular geometry from scattering physics to achieve significant computational speedups and memory reductions while maintaining exact conservation laws and high precision.
This paper proposes a history-aware adaptive reduced-order modeling framework using incremental singular value decomposition (iSVD) that dynamically updates basis functions via occasional full-order corrections, demonstrating superior predictive accuracy and computational efficiency over existing methods for complex nonlinear problems like the Burgers equation, Sod shock tube, and rotating detonation engines.
This paper presents a unified -scaling framework for second-order perturbation theory by combining block tensor decomposition and canonical polyadic decomposition, which achieves high accuracy for MP2 and rPT2 calculations while reducing storage requirements to .
This paper introduces Sketch Tomography, a provably convergent quantum state tomography method that hybridizes classical shadow protocols with matrix product state assumptions to achieve quadratic sample complexity and superior accuracy in observable estimation compared to standard classical shadows and maximum likelihood estimation.