Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …

Abstract Machine learning models are increasingly used in many engineering fields thanks
to the widespread digital data, growing computing power, and advanced algorithms. The …

Recent work in scientific machine learning has developed so-called physics-informed neural
network (PINN) models. The typical approach is to incorporate physical domain knowledge …

We formulate a general framework for hp-variational physics-informed neural networks (hp-
VPINNs) based on the nonlinear approximation of shallow and deep neural networks and …

The design of heterogeneous catalysts is necessarily surface‐focused, generally achieved
via optimization of adsorption energy and microkinetic modelling. A prerequisite is to ensure …

Thermophysical properties of fluid mixtures are important in many fields of science and
engineering. However, experimental data are scarce in this field, so prediction methods are …

We present the first hard-constraint neural network model for predicting activity coefficients
(HANNA), a thermodynamic mixture property that is the basis for many applications in …

Abstract Machine learning models have been successfully used in many scientific and
engineering fields. However, it remains difficult for a model to simultaneously utilize domain …

With the availability of petabytes of oceanographic observations and numerical model
simulations, artificial intelligence (AI) tools are being increasingly leveraged in a variety of …

This paper introduces JAX-FEM, an open-source differentiable finite element method (FEM)
library. Constructed on top of Google JAX, a rising machine learning library focusing on high …