Skip to content

Author

P. V. Coveney

We have 6 of 42 papers

We haven’t gathered this author’s papers yet. Follow them and we’ll fetch their work.

Not the right person? Other researchers publish under this name.

Preprint Sep 2026

Reduced Subgrid-Scale Terms for Turbulence Simulations with the Lattice Boltzmann Method

Turbulent flows span a wide range of scales, making direct numerical simulation (DNS) prohibitively expensive at high Reynolds numbers. Large eddy simulation (LES) offers a pragmatic alternative by only resolving the large-scale motions, but its accuracy hinges on the subgrid-scale (SGS) model. We introduce the Tau-ort...

Rik Hoekstra, Xiao Xue, P. V. Coveney et al. · 0 citations
Preprint Jul 2026

Irreversibility, equilibrium and measurement in quantum mechanics

Quantum mechanics is widely recognised as being incomplete. It is not consistent with the second law of thermodynamics and does not provide a scientifically credible physical account of the measurement process, the means by which coherence is broken and classically observable states are recorded. This has led to many a...

C. Coveney, P. V. Coveney · 0 citations
Preprint Jul 2026

Explainable quantum-compressed machine learning for complex fluid flows

On two patient-specific cardiovascular benchmarks, the structured QCML propagator matches the predictive accuracy of its classical counterpart on surface pressure spectra, pressure drop, and wall shear stress, establishing QCML as a working component of scientific machine learning and a concrete contribution towards pr...

Xiao Xue, Maida Wang, Mingyang Gao et al. · 0 citations
Open access Jul 2026

Fast-forward prediction of lattice Boltzmann dynamics with physics-informed neural operators

A physics-informed neural operator framework is introduced that predicts the LBE evolution over large time jumps without performing step-by-step forward integration, bypassing the need to solve the collision kernel explicitly.

Xiao Xue, M. T. ten Eikelder, Mingyang Gao et al. · 0 citations
Preprint Aug 2026

TNASS: Tensor Network Active Space Selection with the Entanglement Feature

The results demonstrate that this approach leads to lower ground state energies and more accurate dipole moments than other fully automated selection schemes such as those based solely on single-orbital entropy or the selection of spatial orbitals around the HOMO/LUMO gap.

Angus Mingare, Isabelle Heuzé, P. V. Coveney · 0 citations
Preprint Jul 2026

The arrow of time, irreversibility, equilibrium and measurement in quantum mechanics

Quantum mechanics is widely recognised as being incomplete. It is not consistent with the second law of thermodynamics and does not provide a scientifically credible physical account of the measurement process, the means by which coherence is broken and classically observable states are recorded. This has led to many a...

C. Coveney, Peter V. Coveney · 0 citations

We use cookies to run the site and, with your consent, for analytics and to show ads. See our Cookie Policy.