Sprat: Hierarchies of Domain-Specific Languages for Marine Ecosystem Simulation Engineering

Scientific software is becoming more complex and its development nowadays is often an interdisciplinary process in which usually scientists - most of them without training in software engineering - implement the software themselves. To help them achieve a good code quality, we propose to employ a process called Sprat based on the concept of hierarchies of domain-specific languages (DSLs). In such a hierarchy, every DSL constitutes an implementation platform for the DSL directly above it. Each role in the development process (i.e., a scientist from a specific discipline) implements a layer of the software in a DSL belonging to the hierarchy. Therefore, the scientists only deal with abstractions they are familiar with from their respective domain and a clear separation of components is attained. To evaluate the Sprat approach, we present its application to the development of the Sprat simulation - a marine ecosystem model for long-term fish stock prediction

Componentising a scientific application for the grid

CoreGRID is a Network of Excellence funded by the European Commission under the Sixth Framework Programm

Object-oriented hyperbolic solver on 2D-unstructured meshes applied to the shallow water equations

Fluid dynamics, like other physical sciences, is divided into theoretical and experimental branches. However, computational fluid dynamics (CFD) is third branch of Fluid dynamics, which has aspects of both the previous two branches. CFD is a supplement rather than a replacement to the experiment or theory. It turns a computer into a virtual laboratory, providing insight, foresight, return on investment and cost savings1. This work is a step toward an approach that realise a new and effective way of developing these CFD models

Working Notes from the 1992 AAAI Workshop on Automating Software Design. Theme: Domain Specific Software Design

The goal of this workshop is to identify different architectural approaches to building domain-specific software design systems and to explore issues unique to domain-specific (vs. general-purpose) software design. Some general issues that cut across the particular software design domain include: (1) knowledge representation, acquisition, and maintenance; (2) specialized software design techniques; and (3) user interaction and user interface

The Layer-Oriented Approach to Declarative Languages for Biological Modeling

We present a new approach to modeling languages for computational biology, which we call the layer-oriented approach. The approach stems from the observation that many diverse biological phenomena are described using a small set of mathematical formalisms (e.g. differential equations), while at the same time different domains and subdomains of computational biology require that models are structured according to the accepted terminology and classification of that domain. Our approach uses distinct semantic layers to represent the domain-specific biological concepts and the underlying mathematical formalisms. Additional functionality can be transparently added to the language by adding more layers. This approach is specifically concerned with declarative languages, and throughout the paper we note some of the limitations inherent to declarative approaches. The layer-oriented approach is a way to specify explicitly how high-level biological modeling concepts are mapped to a computational representation, while abstracting away details of particular programming languages and simulation environments. To illustrate this process, we define an example language for describing models of ionic currents, and use a general mathematical notation for semantic transformations to show how to generate model simulation code for various simulation environments. We use the example language to describe a Purkinje neuron model and demonstrate how the layer-oriented approach can be used for solving several practical issues of computational neuroscience model development. We discuss the advantages and limitations of the approach in comparison with other modeling language efforts in the domain of computational biology and outline some principles for extensible, flexible modeling language design. We conclude by describing in detail the semantic transformations defined for our language