1259 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 proposes a machine-learning-based initialization method using kernel ridge regression to overcome the nonlinear optimization bottleneck in Hamiltonian-diagonalization dynamical mean-field theory, significantly improving the speed, robustness, and convergence of bath fitting for both non-interacting and interacting systems.
This paper introduces a novel framework that enables exact discrete stochastic simulation of continuous-time Markov chains to be differentiable and massively parallel by decoupling hard categorical sampling from gradient propagation via a Gumbel-Softmax surrogate, thereby achieving deep-learning-scale optimization and high-dimensional parameter inference across diverse scientific domains.
This paper demonstrates that while deterministic closures fail to capture the rapid growth of uncertainty in coarse-grained turbulence models, incorporating data-driven stochastic closures is essential for accurately restoring the correct timing and magnitude of variance growth across scales.
This paper introduces a robust multistep thermodynamic integration framework combining replica exchange, machine learning potentials, and validated DFT functionals to accurately compute the Gibbs free energy of metal halide perovskites, revealing that their phase stability is primarily governed by ground-state energy differences rather than material-specific thermal effects.
Using a newly proposed unbiased energy-gap estimator in projection quantum Monte Carlo simulations, this study reveals that while the minimum energy gap in two-dimensional Edwards-Anderson spin glasses exhibits unfavorable super-algebraic scaling with infinite variance, the all-to-all Sherrington-Kirkpatrick model maintains a finite-variance distribution with a slow power-law scaling (), suggesting greater potential for quantum annealing efficiency in densely connected optimization problems.
This paper proposes a Bayesian estimation method to optimize the measurement window in synchrotron-radiation-based Mössbauer spectroscopy, demonstrating that this approach improves the precision of center shift measurements by more than three times compared to conventional Lorentzian fitting.
This paper introduces a reinforcement learning-based framework that effectively characterizes and emulates quantum hardware noise to bridge the gap between simulations and real superconducting qubit execution, offering greater flexibility than conventional modeling techniques.
This paper proposes an improved method for estimating partial differential and delay partial differential equations from data by integrating time integration with Bayesian optimization and the Bayesian information criterion to automatically optimize hyperparameters, thereby enhancing robustness and expanding the modeling scope across various physical systems.
This paper proposes a system-oriented, symmetry-adapted even-tempered basis set design that uses primitive S-subshell Gaussian-type orbitals and only two parameters to efficiently and accurately encode electronic ground-state information, achieving high-quality results for hydrogen systems with reduced optimization costs and improved scalability.
This paper introduces a novel amortized inference technique using likelihood-weighted Normalizing Flows that overcomes the limitations of standard unimodal base distributions in capturing multi-modal posteriors by initializing the flow with a Gaussian Mixture Model, thereby enabling efficient and accurate parameter estimation in high-dimensional inverse problems without requiring posterior training samples.