The use of deep neural networks for modelling system dynamics is increasingly popular, but long-term prediction accuracy and out-of-distribution generalization still present challenges. In this study, we address these challenges by considering the parameters of dynamical systems as factors of variation of the data and leverage their ground-truth values to disentangle the representations learned by generative models. Our experimental results in phase-space and observation-space dynamics, demonstrate the effectiveness of latent-space supervision in producing disentangled representations, leading to improved long-term prediction accuracy and out-of-distribution robustness.

Generating and managing input data for large-scale scientific computations has, for many classes of application, always been a challenging process. The emergence of new hardware platforms and increasingly complex scientific models compounds this problem as configuration data can change depending on the underlying hardware and properties of the computation. In this paper we present TemPSS (Templates and Profiles for Scientific Software), a web-based service for building and managing application input files using the concepts of software parameter templates and job profiles. Many complex, distributed applications require the expertise of more than one individual to allow an application to run efficiently on different types of hardware. TemPSS supports collaborative development of application configurations through the ability to save, edit and extend job profiles that define the inputs to an application. We describe the process of defining templates and profiles and the structures used to add an application template to the TemPSS service. We also detail the implementation of the service and describe its interfaces and functionality.

As the capabilities and diversity of computational platforms continue to grow, scientific software is becoming ever more complex in order to target resources effectively. In the libhpc project we are developing a suite of tools and services to simplify job description and execution on heterogeneous infrastructures. This paper presents Nekkloud, a web-based software environment, built on aspects of the libhpc framework, for running the Nektar++ high-order finite element code on both cluster and cloud platforms, while improving the accessibility of the software for end-users and improving the user experience. Nektar++ provides a suite of solvers which span a range of scientific domains, ensuring that Nekkloud has a broad range of use cases. We describe the Nekkloud environment, its use and its ability to target both local campus cluster infrastructure and cloud computing resources, enabling users to make better use of the facilities available to them.