Computational sciences

Scientific research is traditionally based on observations (experiments) and theory to understand, predict, and control a phenomenon at hand.

With the emergence of increasingly powerful computers, the scientific approach takes more and more advantages of high performance computing, mathematical modeling, and simulation techniques to achieve its goals.

The goal of CADMOS is not only to offer powerful computational resources to the scientific community of the Lake Geneva area, but also to develop and promote computational science across all disciplines; be it for the basic sciences, the natural sciences, or the humanities.

Computational science offers a new scientific approach. Gradually developing from the original need to solve more and more involved problems, computational science has begun to establish itself as an independent discipline. In addition to the development of highly efficient algorithms for solving mathematical problems, it is also essential to establish novel methods for solving problems that cannot be tackled by classic approaches. This is, for example, due to the fact that the observed phenomena are often the result of interactions between many different components that act across different scales.

The emergence of collectives properties (the fact that “the whole is greater than the sum of its parts”) is the signature of what is called a complex system. In fact, nowadays complex systems are the rule rather than the exception in applications. One of the best ways to understand such a system is through a computer simulation of its components. It is thus possible to replicate, for example, a part of the universe containing the earth, as means to study earth’s role in the cosmos. Consequently, computational science offers the ability to perform virtual experiments, in which all degree of freedoms for an arbitrary phenomena are fully controllable. Of course, the computational power needs to increase when the level of detail is raised by incorporating more elements into the same simulation.

Like mathematics offers many tools to describe nature, computational science offers an ever-growing range of numerical methods for modeling complex phenomena: discrete event simulations, cellular automata, multi-agent simulation, and machine learning are just a few examples of non-standard tools that belong to the toolbox of modern computational science.

With the emergence of increasingly powerful computers, the scientific approach takes more and more advantages of high performance computing, mathematical modeling, and simulation techniques to achieve its goals.

The goal of CADMOS is not only to offer powerful computational resources to the scientific community of the Lake Geneva area, but also to develop and promote computational science across all disciplines; be it for the basic sciences, the natural sciences, or the humanities.

Computational science offers a new scientific approach. Gradually developing from the original need to solve more and more involved problems, computational science has begun to establish itself as an independent discipline. In addition to the development of highly efficient algorithms for solving mathematical problems, it is also essential to establish novel methods for solving problems that cannot be tackled by classic approaches. This is, for example, due to the fact that the observed phenomena are often the result of interactions between many different components that act across different scales.

The emergence of collectives properties (the fact that “the whole is greater than the sum of its parts”) is the signature of what is called a complex system. In fact, nowadays complex systems are the rule rather than the exception in applications. One of the best ways to understand such a system is through a computer simulation of its components. It is thus possible to replicate, for example, a part of the universe containing the earth, as means to study earth’s role in the cosmos. Consequently, computational science offers the ability to perform virtual experiments, in which all degree of freedoms for an arbitrary phenomena are fully controllable. Of course, the computational power needs to increase when the level of detail is raised by incorporating more elements into the same simulation.

Like mathematics offers many tools to describe nature, computational science offers an ever-growing range of numerical methods for modeling complex phenomena: discrete event simulations, cellular automata, multi-agent simulation, and machine learning are just a few examples of non-standard tools that belong to the toolbox of modern computational science.