Data assimilation (DA) and uncertainty quantification (UQ) are extensively used in analysing
and reducing error propagation in high-dimensional spatial-temporal dynamics. Typical …

This article proposes a new framework using physics-informed neural networks (PINNs) to
simulate complex structural systems that consist of single and double beams based on Euler …

Turbulence remains a problem that is yet to be fully understood, with experimental and
numerical studies aiming to fully characterize the statistical properties of turbulent flows …

The last decade has seen a rise in the number and variety of techniques available for data-
driven simulation of physical phenomena. One of the most promising approaches is Physics …

We propose a data-driven, closure model for Reynolds-averaged Navier-Stokes (RANS)
simulations that incorporates aleatoric, model uncertainty. The proposed closure consists of …

The thermal boundary conditions in a numerical simulation for heat transfer are often
imprecise. This leads to poorly defined boundary conditions for the energy equation. The …

This paper presents data-driven simulations of two-phase fluid processes with heat transfer.
A Physics-Informed Neural Network (PINN) was applied to capture the behaviour of phase …

The spatiotemporal resolution of Partial Differential Equations (PDEs) plays important roles
in the mathematical description of the world's physical phenomena. In general, scientists …

Recent years have seen a surge in deep learning approaches to accelerate numerical
solvers, which provide faithful but computationally intensive simulations of the physical …

Physics informed neural networks (PINNs) have demonstrated their effectiveness in solving
partial differential equations (PDEs). By incorporating the governing equations and …