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 study utilizes an observational dataset and Random Forest modeling to quantify how atmospheric conditions, particularly moisture convergence, instability, and water vapor, nonlinearly control the spatial and seasonal variability of mesoscale convective systems over tropical oceans, explaining up to 50% of their frequency and precipitation variance.
This study utilizes satellite-based indices and long-term MCS tracking data to demonstrate a robust two-way feedback mechanism where the Madden-Julian Oscillation organizes mesoscale convective systems through environmental modulation, while the collective upscale impacts of these systems actively reinforce the MJO's circulation and support its eastward propagation.
This paper reviews data-driven and machine learning approaches, particularly descriptor-based models and interatomic potentials trained on DFT data, that accelerate point defect simulations in solid-state materials by enabling rapid, quantum-mechanically accurate predictions of properties like formation energies and vibrational free energies for high-throughput screening and experimental integration.
This paper introduces DiffSRDA, a computationally efficient, uncertainty-aware data assimilation framework based on denoising diffusion models that generates high-resolution ensemble analyses from low-resolution forecasts and sparse observations, achieving performance comparable to high-cost methods while supporting training-free adaptation to changing sensor layouts.
This paper introduces JAX-BEM, a differentiable Boundary Element Method solver built on the JAX framework that matches the accuracy of traditional codes while enabling efficient gradient-based acoustic shape optimization and inverse problem solving.
This paper introduces a flexible, integrated interface within GROMACS that enables hybrid machine learning/molecular mechanics simulations using PyTorch-trained neural network potentials, facilitating their seamless application to diverse biomolecular systems and advanced sampling workflows.
This paper introduces Pi-PINN, a transferable learning framework that leverages a shared physics-informed embedding space and closed-form pseudoinverse head adaptation to solve diverse partial differential equations rapidly and accurately with minimal or no training data for unseen instances.
This paper proposes an agentic AI framework that integrates large language models with lightweight physics-based models to autonomously evaluate and optimize thermal comfort and building energy performance in tropical urban environments through rapid, closed-loop reasoning and action.
This paper presents a meshless -adaptive numerical method for simulating non-Newtonian natural convection in a differentially heated cavity, demonstrating that dynamically adjusting node density based on shear-thinning flow characteristics significantly enhances computational efficiency while accurately capturing sharp boundary layer structures.
This study investigates the finite-temperature phase diagram of two-dimensional soft-core bosons using self-consistent Hartree-Fock and quantum Monte Carlo methods, identifying a broad supersolid phase and a potential intermediate hexatic phase while validating mean-field theory as an effective tool for analyzing these transitions.