Scientific discovery and engineering design are currently limited by the time and cost of
physical experiments. Numerical simulations are an alternative approach but are usually …

Computationally efficient surrogates for parametrized physical models play a crucial role in
science and engineering. Operator learning provides data-driven surrogates that map …

Operator learning is a variant of machine learning that is designed to approximate maps
between function spaces from data. The Fourier Neural Operator (FNO) is a common model …

We propose a new neural network based method for solving inverse problems for partial
differential equations (PDEs) by formulating the PDE inverse problem as a bilevel …

Background and objective Bacteriuria is anticipated in long-term indwelling catheter (IDC)
use, and urinary tract infections (UTIs) and related issues are common. Defence …

While predicting the flow behavior for a well-defined particulate microstructure is fairly
tractable, systematic tailoring of the particle properties to yield desired flows remains illusive …

Aims The integration of artificial intelligence (AI) into functional urology management must
be assessed for its clinical utility, but hopefully will change, perhaps to revolutionize the way …

Enforcing physics constraints in surrogate models for PDE evolution operators can improve
the physics plausibility of their predictions and their convergence and generalization …

Understanding the effect of intricate surface wettability conditions on microswimmers is
crucial for precisely navigating them across narrow microcirculatory networks. Here, we …

Transport and dispersion of active particles in structured environments, such as corrugated
channels and porous media, are important for the understanding of both natural and …