Partial differential equations (PDEs) see widespread use in sciences and engineering to
describe simulation of physical processes as scalar and vector fields interacting and …

Partial differential equations (PDEs) are central to describing complex physical system
simulations. Their expensive solution techniques have led to an increased interest in deep …

We introduce a compositional physics-aware FInite volume Neural Network (FINN) for
learning spatiotemporal advection-diffusion processes. FINN implements a new way of …

In recent engineering applications using deep learning, physics-informed neural network
(PINN) is a new development as it can exploit the underlying physics of engineering …

The rapid development of deep learning has significant implications for the advancement of
computational fluid dynamics. Currently, most pixel-grid-based deep learning methods for …

Despite rapid improvements in the performance of the central processing unit (CPU), the
calculation cost of simulating chemically reacting flow using CFD remains infeasible in many …

Previous work on learning physical systems from data has focused on high-resolution grid-
structured measurements. However, real-world knowledge of such systems (eg weather …

Many physical processes such as weather phenomena or fluid mechanics are governed by
partial differential equations (PDEs). Modelling such dynamical systems using Neural …

Despite the rapid growth of CPU performance, the computational cost to simulate the
chemically reacting flow is still infeasible in many cases. There are few studies to accelerate …

When modeling physical processes in spatially confined domains, the boundaries require
distinct consideration through specifying appropriate boundary conditions (BCs). The finite …