Computational Physics Papers of the Week

Spatially resolved modeling of optically pumped alkali-metal vapors requires the solution of a high-dimensional nonlinear diffusive quantum master equation (DQME) coupled to state-dependent light propagation. We develop a structure-preserving computational framework for this coupled three-dimensional system in the high-pressure limit.

We introduce Fracture-Bench, an open benchmark of two phase-field datasets with fixed splits, common test metrics and a description of each dataset's physical regime.

We present a geometric interface reconstruction method for Volume-of-Fluid (VOF) simulations that captures both curved interfaces and sharp features using only volume fraction data.

We apply the transient subtraction method to the computation of the mobility and the shear viscosity in a Lennard-Jones fluid in DPD.

We introduce ROSE, a deep-learning exchange--correlation energy functional built from the occupied-state density matrix.

In this paper, we use the Neural Discrete Equilibrium (NeurDE) method to explore stable emulation at extremely coarse grids, demonstrating physically-principled control over entropy dynamics that reinforces the physical system's dissipative mechanism.

We introduce a deterministic, instance-specific upper bound on the number of binary configurations whose energies improve upon a returned state by a prescribed margin.

We present an axisymmetric continuous simplex space-time finite element method for incompressible fluid flow.

Here, we demonstrate that existing strategies based purely on features derived from an external electric field fail to distinguish certain environments, ultimately limiting their accuracy. As a solution, we propose an extension to the equivariant TensorNet architecture that mitigates this shortcoming by incorporating classical atoms close to the quantum region into the message-passing scheme, combined with features derived from the electric field for describing the environment farther away.

The lattice Boltzmann (LB) method is widely regarded as a very flexible and efficient solver for a broad class of transport phenomena in fluids, soft matter and related fields. In this paper, we posit that such flexibility is a direct result of its generative nature as a rule-driven information-processing machine.

Materials design is a multi-objective problem, and recent advances in quantum optimization make it possible to bring quantum solvers to bear on such problems.

We evaluate the approach on controlled and public benchmarks and a data-centre thermal simulation dataset (DCTS) containing million-cell meshes.

The theory introduces an independent compliance-damage field, which accommodates both diffuse distributions and singular concentrations.

Here, we introduce a knowledge-guided framework for dimensionless learning, called {\pi}-former.

We demonstrate the approach for hydrogen chains on a quantum simulator and for azobenzene on a quantum processor.

Directed percolation (DP) provides a canonical framework for studying nonequilibrium phase transitions into absorbing states, yet extracting critical behavior directly from high-dimensional spatiotemporal configurations remains challenging.

We present a generalized variational approach for constructing energy-stable macroscopic partial differential equations (PDEs) directly from microscopic physical laws.

However, standard monolithic PINNs for incompressible Navier-Stokes (NS) equations often suffer from severe constraint coupling and spectral biases, limiting both convergence and accuracy. To address this challenge, we propose a segregated, iterative, and multi-stage PINN (SIM-PINN) inspired by the classical projection method in computational fluid dynamics.

We introduce AMPERe, a quadrature-by-expansion (QBX) evaluator for vector magnetostatic kernels on general three-dimensional, non-axisymmetric tokamak and stellarator geometries.

However, simultaneously generating stable and novel structures remains challenging because of the complex structural and physical constraints governing crystals. To address this challenge, we propose site-orbit masked twin-branch Transformer model (ST2M), a symmetry-guided discrete--continuous diffusion framework incorporating degree-of-freedom masks and conditioned on Wyckoff patterns.