Computational physics
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- Effective Graph Neural Representation of Chain Architectures of Block Copolymers Tested by Predicting RPA Instability Limits
- Learning to reconstruct Germano Differential Filtered Fields with Convolutional Neural Networks for Large Eddy Simulation of Plasma Turbulence
- Online reduced-order modeling from primal solver trajectories for PDE-constrained optimization
- GeONet: Geometry-Encoded Operator Network for Predicting Flow in Heterogeneous Porous Media
- A nonequilibrium-capturing unified gas-kinetic scheme I: discrete orthogonal basis construction and expansion on velocity space
- A Parameter-Free Reference Hamiltonian for Tight-Binding Vanadium Oxides
- How deterministic trajectories and their fluctuations underlie allele-frequency statistics in the strong-selection regime
- Structured Multi-Level-Set Inversion for 3-D Electromagnetic Inverse Scattering
- Anomaly detection for multijet scenarios
- GW reduced density matrix from iterated linearized Dyson equation
- Intrinsic instantaneous coarse-to-fine recoverability in multiscale chaos: The Lorenz-96 system
- The BRUIT-FM "open data" noise reduction challenge
- Static spin exchange-correlation kernel of the uniform electron gas at finite temperature
- LAO+: Focused, Multi-Replica Accelerated Orthogonal Sampling for Robust Binding Free Energy Predictions
- Advanced computational methods for simulating the classical hydrogen atom in stochastic electrodynamics
- Global-in-time Spectral Duhamel-Picard Method for Post-Exposure Bake Simulation and Physics-Informed Neural Operators for EUV Lithography
- Standardized Planck-Mean Absorption Coefficients for High-Temperature Gases
- A deep energy method formulation for phase-field modelling of ductile fracture
- A quantum-assisted latent surrogate model for Bayesian inversion of heterogeneous material properties on NISQ hardware
- A Unified Framework for Bell-Shaped Gaussian Quadratures in Deterministic Boltzmann Solvers
- High-fidelity k·p representations of first-principles electronic band structures through covariant renormalization
- A Transferable Machine-Learned Potential Enabling Large-Scale Copper-Carbon Simulations from Equilibrium to Extreme Conditions
- Local-resolvent formulation and assessment of linear-scaling Kohn-Sham calculations
- Can Machine Learning Potentials Be More Accurate Than the Methods They Are Trained On?
- All-electron machine-learned orbital-free density functional for periodic CHNO systems