This paper presents a complete dynamic modeling of a membrane distillation process. The model contains two consistent dynamic models. A 2D advection-diffusion equation for modeling the whole process and a modified heat equation for modeling the membrane itself. The complete model describes the temperature diffusion phenomenon across the feed, membrane, permeate containers and boundary layers of...

Rich and complex time-series data, such as those generated from engineering systems, financial markets, videos, or neural recordings are now a common feature of modern data analysis. Explaining the phenomena underlying these diverse data sets requires flexible and accurate models. In this paper, we promote Gaussian process dynamical systems as a rich model class that is appropriate for such an ...

The computational design of dynamical systems is an important emerging task in synthetic biology. Given desired properties of the behaviour of a dynamical system, the task of design is to build an in-silico model of a system whose simulated be- haviour meets these properties. We introduce a new, process-based, design methodology for addressing this task. The new methodology combines a flexible ...

An air parcel model has been used to study dynamic influences on cirrus cloud microphysical processes. Representative data selected from a measurement campaign carried out over southern Germany during March 1994 were used for a base-case model run where a modeled air parcel moved in a wave trajectory with a period similar to the measured Brunt–Väisälä frequency and an amplitude of about 30 m. S...

Recent work has shown it to be possible to integrate model building and verification. This is a desirable approach which can yield systems that are correct by design, an elegant solution to common industry demands [1]. Traditionally, we can utilise Bayes’ rule to make predictions on confidence of model parameters given data that satisfy a given property of interest. The interest can be expresse...