Computational physics
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- Chiral vibrational modes and vibrational circular dichroism
- Photon Calibration Techniques for High Resolution Cryogenic Detectors
- Generalizing Density Functional Theory with a Particle-Hole Expansion
- NEOPIC: A Neural Operator Framework for Particle-based Kinetic Plasma Simulations
- Single-Temperature Ranked Greedy Cluster Exchange for Max-Cut on Two-Dimensional Tori
- Predicting Weight Windows for Duct-Streaming Shielding Geometries with a Convolutional Neural Network
- ML-COHP: predicting bond descriptors using latent space of a universal interatomic potentials
- Electric field control of anisotropic spin traffic between spin-filtering and magnon-scattering in a layered antiferromagnet
- A Gaussian-Process Surrogate for Control Rod Worth with Calibrated Uncertainty: Application to VERA Core Physics Benchmark Problem 4
- posterior-landscape: morphological feature finding in two-dimensional probability densities
- Wave Turbulence of Capillary Waves in Finite Systems
- Beyond χ2: Ensemble null tests for reliable scientific discovery
- Adiabatic Variational Principle: Born-Oppenheimer Theory for Emergent Electronic-Order Dynamics
- Accelerating fits in high-energy physics and beyond
- Sub-monolayer tracking of molecular states at buried interfaces
- Static Response of the Strongly Coupled Electron Gas from Diffusion Monte Carlo
- Benchmarking Quantum Neural Networks Developed for MBHB Coalescence in the Context of LISA Space Mission
- Excitons in solid molecular hydrogen from quantum Monte Carlo
- RAKI: High-Order Alternative Finite Differences for General Relativistic Magnetohydrodynamics
- Optimization of curved vortex generator geometry for enhanced thermal performance in water-to-air finned-tube heat exchanger
- Transformer-inspired architectures make machine-learned electron densities accurate and transferable enough to bypass self-consistency
- Hidden Phase Transition in Single-Polymer Stretching under Elongational Flow
- Distilling Large-Scale Machine Learning Interatomic Potentials to Predict Long-Timescale Electrolyte Transport Properties
- Itô versus Stratonovich for shot noise with state-dependent amplitude: why the answer is Marcus
- Ten-loop fixed-dimension renormalization-group functions of the three-dimensional O(N) model from distributed GPU-accelerated parametric integration