1201 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 GPU-accelerated, quantum-inspired fluid simulation method using matrix product states (MPS) and the cuQuantum library to efficiently model 2D turbulence at high Reynolds numbers, demonstrating up to a 12.1-fold speedup over traditional approaches and establishing a theoretical scaling law for the required bond dimension based on turbulent energy spectra.
This paper presents an enhanced genetic algorithm for crystal structure prediction that integrates a universal neural network potential with diversity-preserving mechanisms, such as niching and aging, to efficiently explore multicomponent composition spaces and accurately reproduce phase diagrams with fewer computational trials than existing methods.
This paper presents a flexible Bayesian method for estimating the power spectral density of LISA noise by combining parametric models with adaptive penalized B-splines, achieving high accuracy and computational efficiency suitable for long-duration mission data analysis.
This paper presents a fault-tolerant, end-to-end quantum algorithm that reformulates topology optimization as a combinatorial problem solved via Grover's search, utilizing quantum finite-element methods to achieve a quadratic speedup over classical unstructured search in finding optimal structural designs.
CaloClouds3 is an ultra-fast, geometry-independent generative model that utilizes angular conditioning and position-agnostic training data to simulate photon showers across an entire high-granularity detector barrel, achieving a two-order-of-magnitude speedup over Geant4 while maintaining full compatibility with physics reconstruction chains.
This study utilizes high-resolution fluid-structure interaction simulations to demonstrate that while hydrodynamic actuation monotonically affects oil droplet mobilization time in deformable constricted tubes, dynamic wall actuation offers superior control through a resonance effect that minimizes transport time near the tube's natural frequency.
This paper demonstrates that plasmonic toroidal nanoantennae can achieve programmable, near-complete switching (99.9% modulation) of molecular transition energies through Fano interference, offering a high-sensitivity architecture for applications ranging from biosensing to quantum computing.
This paper introduces the Discrete Variable Circuit Quantum-Classical Physics-Informed Neural Network (QCPINN) and demonstrates its superior accuracy over classical PINNs in solving four complex reservoir seepage models by leveraging quantum circuit topologies to enhance high-dimensional feature mapping and physical constraint embedding.
The paper introduces MultiAtomLiouvilleEquationGenerator (MulAtoLEG), an open-source Mathematica package that efficiently generates exact Liouville superoperators and master equations for multilevel atomic systems and general Hamiltonians by leveraging vectorization and sparse linear algebra.
This paper introduces a foundational implicit solvent model that distills evolutionary knowledge from the protein language model ESM3 into a graph neural network, enabling accurate and stable molecular dynamics simulations of both folded and intrinsically disordered proteins.