physics.comp-ph papers | Gist.Science

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.

High-Dimensional Enhanced Sampling via Regularized Path-Dependent McKean--Vlasov Dynamics using Tensor Density Approximation

This paper proposes a scalable, regularized path-dependent McKean-Vlasov framework for high-dimensional enhanced sampling that improves statistical stability through path-history measures and achieves efficient numerical realization via optimization-free tensor density approximation, enabling effective exploration of complex energy landscapes with collective variable dimensions up to 64.

Liyao Lyu, Siyu Guo, Huan Lei2026-05-06🔢 math

Energy dissipation at the atomic scale explains how fracture energy depends on crack velocity in silica glass

Using molecular dynamics simulations with a machine-learned potential, this study reveals that the fracture energy of silica glass increases by up to 33% below the branching threshold due to a combination of rising intrinsic surface energy density and nanoscale roughening, demonstrating that dynamic fracture creates a fundamentally different surface structure rather than merely increasing apparent surface area.

Marthe Grønlie Guren, Sigbjørn Løland Bore, François Renard, Henrik Andersen Sveinsson2026-05-06🔬 cond-mat.mtrl-sci

Solving Systems of Linear Equations: HHL from a Tensor Networks Perspective

This paper introduces a novel tensor network-based approach to efficiently simulate the HHL algorithm in the qudit formalism, benchmarking its performance against exact inversion and Qiskit implementations while analyzing its sensitivity to hyperparameters to establish a noise-free upper bound for the algorithm's computational efficiency.

Alejandro Mata Ali, Iñigo Perez Delgado, Marina Ristol Roura, Aitor Moreno Fdez. de Leceta, Sebastián V. Romero2026-05-05⚛️ quant-ph

Quantum-inspired Techniques in Tensor Networks for Industrial Contexts

This paper evaluates the applicability, feasibility, and scalability of quantum-inspired tensor network algorithms for industrial use cases by reviewing existing literature and analyzing potential applications alongside their inherent limitations.

Alejandro Mata Ali, Iñigo Perez Delgado, Aitor Moreno Fdez. de Leceta2026-05-05⚛️ quant-ph

VeloxQ: A Fast and Efficient QUBO Solver

The paper introduces VeloxQ, a fast and scalable classical solver for QUBO and HUBO problems that demonstrates competitive performance and superior scalability on large sparse instances compared to state-of-the-art quantum annealers, physics-inspired algorithms, and conventional optimization methods.

J. Pawłowski, J. Tuziemski, P. Tarasiuk, H. Louzada, R. Adamski, K. Hendzel, Ł. Pawela, B. Gardas2026-05-05⚛️ quant-ph

Numerical and Experimental Evaluation of Chip Evacuation and Lubricant Flow using Optimized Drill Heads for Ejector Deep Hole Drilling

This study demonstrates that additively manufactured, flow-optimized drill heads significantly reduce the minimum fluid flow required for stable ejector deep hole drilling by minimizing vortex formation and improving chip evacuation, as validated through combined smoothed particle hydrodynamics simulations and experimental testing.

Nuwan Rupasinghe, Sebastian Michel, Andreas Baumann, Julian Gerken, Samuel Gülde, Dirk Biermann, Peter Eberhard2026-05-05🔬 physics

Multi-Objective Evolutionary Design of Molecules with Enhanced Nonlinear Optical Properties

This study evaluates various evolutionary algorithms for designing molecules with enhanced nonlinear optical properties, finding that while NSGA-II excels at optimizing specific objective scores, the MOME method superiorly balances solution quality with structural diversity, as evidenced by higher global hypervolume and MOQD metrics.

Dominic Mashak, Jacob Schrum, S. A. Alexander2026-05-05🔬 physics

Physics-informed neural networks for form-finding of unilateral membrane structures

This paper demonstrates that Physics-Informed Neural Networks (PINNs) serve as a viable alternative to traditional Finite Element Methods for the form-finding of unilateral membrane structures, with a hard-boundary condition formulation proving superior in accuracy and residual smoothness compared to a soft-boundary approach.

Luigi Sibille, Sigrid Adriaenssens, Carlo Olivieri2026-05-05💻 cs

Stochastic Cluster Expansion for Excited State Energies

This paper extends the stochastic cluster expansion framework to excited states, enabling accurate calculation of excitation gaps in strongly correlated systems by expressing energy differences as a hierarchy of orbital-space cluster contributions that eliminate the need for large preselected active spaces.

Annabelle Canestraight, Russell Miller, Libor Veis, Vojtech Vlcek2026-05-05🔬 physics

Colloidal layer deposition with a controllable number of layers and compositional order

This paper presents a DNA-mediated design for the self-assembly of binary colloidal suspensions that enables precise control over both the number of layers and the compositional order of the resulting crystallites by leveraging equilibrium principles for thickness and engineered reaction kinetics for particle arrangement.

Akshaya Kumar Jena, Aashima Aashima, Pritam Kumar Jana, Bortolo Matteo Mognetti2026-05-05🔬 cond-mat

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