Modelling & Simulation

Mathematical modelling and simulation lie at the heart of Quintessa, whose core competence is in the formulation, development and application of innovative mathematical and statistical models and associated scientific software.

Mathematical modelling and simulation have proved to be powerful tools in the hands of academics for many decades providing quantitative predictions and understanding. In the 21st century, modelling and simulation have emerged as key industrial technologies in support of improved decision making, design, manufacturing and environmental impact assessment. This has been facilitated by the increasing power of successive generations of computers, which has allowed scientists and engineers to focus upon problems of increasing size, complexity and difficulty.

At Quintessa we have a team of lateral thinking mathematicians and scientists who are experienced in solving problems using techniques from diverse fields. We aim to work in partnership with our clients to ensure that appropriate solutions are developed even if the problem is poorly defined at the outset.

A key element of our role as mathematical consultants is to provide an interface between the generators of fundamental knowledge in universities and research institutes, and clients requiring quality solutions to time and budget. By communicating effectively with both academia and industry we are able to provide effective solutions to our clients in a business-like fashion.

While each project is tailored to client needs, elements of the following are common to many projects: client liaison and project planning; problem formulation; information gathering and interpretation; conceptual and mathematical model formulation; mathematical and numerical analysis; software development; testing, verification and validation; application and assessment; synthesis and presentation; project review; and training.

In recent years mathematical and numerical techniques applied by Quintessa staff have included: differential and algebraic equations; probabilistic analysis; statistics; pattern recognition; non-linear mathematics; optimisation; fuzzy logic; rough sets; genetic algorithms; time-series analysis; finite-element, finite-difference and finite-volume analysis; sensitivity and uncertainty analysis; and agent-based modelling.