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.
Using the high-fidelity M3D-C1 code, this study investigates low-n MHD instabilities in SPARC baseline scenarios, revealing that internal kink modes drive sawtooth crashes through magnetic reconnection and pressure mixing, with crash timing highly sensitive to the on-axis safety factor and temperature profiles.
This paper introduces a "freeze-and-release" direct optimization method that successfully achieves variational orbital optimization for excited electronic states, particularly charge transfer excitations, by preventing variational collapse and correctly describing energy dependencies without requiring long-range exact exchange, where conventional maximum overlap methods often fail.
This living document explores the rapidly evolving potential of quantum computing in Thermal Science, initially focusing on heat conduction as a paradigmatic test case to develop novel algorithms and evaluate real hardware performance in the pursuit of quantum supremacy for engineering applications.
This paper introduces Point Group Order Parameters (PGOPs) as a new set of metrics to directly quantify local point-group symmetry in complex particle systems, demonstrating their superior utility in detecting crystalline order compared to traditional bond-orientational parameters and providing their implementation in the open-source SPATULA software package.
This paper introduces Predictor-Driven Diffusion, a novel framework that integrates renormalization-group-based spatial coarse-graining with path-integral temporal dynamics to enable a unified predictor for spatiotemporal generation, simulation, and super-resolution in multiscale turbulent systems.
This paper introduces SAT3-NN, a neural network framework trained on high-resolution CGYRO simulation data that outperforms previous quasilinear saturation models (SAT0–SAT3) by more accurately predicting turbulent fluxes and capturing anti-gyroBohm scaling from linear gyrokinetic inputs.
The paper introduces BuSyNet, a deep learning architecture that integrates dimensional consistency and symplectic geometry to discover interpretable, closed-form symbolic Hamiltonian expressions, achieving superior long-term prediction accuracy and stability on physical systems like the harmonic oscillator and Kepler problem compared to state-of-the-art methods.
This paper demonstrates that neural network emulators of virtual circuits can robustly and effectively control MAST Upgrade plasma shapes in real-time closed-loop simulations, offering a low-latency alternative to traditional physics-based controllers for future fusion devices.
This paper introduces Equitrain, a LoRA-based fine-tuning framework that significantly enhances the accuracy of machine-learning interatomic potentials for predicting phonon and thermal properties across diverse materials using minimal additional training data, outperforming both pretrained and scratch-trained models.
This paper introduces Simulated Bifurcation Quantum Annealing (SBQA), a quantum-inspired optimization algorithm that incorporates inter-replica interactions to mimic quantum tunneling, demonstrating superior performance over the standard Simulated Bifurcation Method on sparse and rugged energy landscapes while maintaining efficiency and versatility across diverse problem families.