We present an application of deep generative models in the context of partial differential
equation constrained inverse problems. We combine a generative adversarial network …

Hardware trends over the last decade show increasing complexity and heterogeneity in high
performance computing architectures, which presents developers of CFD applications with …

Python has become the de facto language for scientific computing. Programming in Python
is highly productive, mainly due to its rich science-oriented software ecosystem built around …

We present an open-source differentiable acoustic simulator, j-Wave, which can solve time-
varying and time-harmonic acoustic problems. It supports automatic differentiation, which is …

Writing software packages for seismic inversion is a very challenging task because
problems such as full-waveform inversion or least-squares imaging are algorithmically and …

This article describes the development of automatic program generation technology to
create scalable phase-field methods for material science applications. To simulate the …

Exascale computing will feature novel and potentially disruptive hardware architectures.
Exploiting these to their full potential is non-trivial. Numerical modelling frameworks …

Full waveform inversion (FWI) and distributed acoustic sensing (DAS) are powerful tools with
potential to improve how seismic site characterization is performed. FWI is able to provide …

Exascale computing brings with it diverse machine architectures and programming
approaches which challenge application developers. Applications need to perform well on a …

Present-day stencil codes are implemented in general-purpose programming languages,
such as Fortran, C, or Java, Python or derivates thereof, and harnesses for parallelism, such …