Hydraulic properties of natural fractures are essential parameters for the modeling of fluid
flow and transport in subsurface fractured porous media. The cubic law, based on the …

We consider multiphysics applications from algorithmic and architectural perspectives,
where “algorithmic” includes both mathematical analysis and computational complexity, and …

Abstract Partial Differential Equations (PDEs) are fundamental to model different
phenomena in science and engineering mathematically. Solving them is a crucial step …

Near-wall blood flow and wall shear stress (WSS) regulate major forms of cardiovascular
disease, yet they are challenging to quantify with high fidelity. Patient-specific computational …

Partial differential equations play a fundamental role in the mathematical modelling of many
processes and systems in physical, biological and other sciences. To simulate such …

In this paper, we use deep feedforward artificial neural networks to approximate solutions to
partial differential equations in complex geometries. We show how to modify the …

Advances in artificial intelligence (AI) are fueling a new paradigm of discoveries in natural
sciences. Today, AI has started to advance natural sciences by improving, accelerating, and …

In the present work, a hyperelastic constitutive model based on neural networks is proposed
which fulfills all common constitutive conditions by construction, and in particular, is …

We present the first application of an artificial neural network trained through a deep
reinforcement learning agent to perform active flow control. It is shown that, in a two …

We develop a method for generating degree-of-freedom maps for arbitrary order Ciarlet-type
finite element spaces for any cell shape. The approach is based on the composition of …