Enhance decision-making using highly accurate simulations.
OUR SOFTWARE
Digital Twin Modelling Software
Pioneering approaches to create models of complex systems capable of real-time behaviour prediction, paving the way for efficient modelling and control of engineering systems in real-world applications.
Real-Time Insights
Integrating sensor data with qualitative models using advanced scientific machine learning and data assimilation techniques, we create a robust framework for real-time digital twinning and control.
Predictive Analytics
Virtual replicas of physical systems make it possible to monitor and analyse operations in real-time, enabling faster and more informed decision-making.
Optimisation
From optimising data centre cooling to improving road transportation systems and advancing renewable energy solutions like wind farms and hydrogen storage, the applications are far-reaching.
BENEFITS
Smarter, Data-driven Decisions.
Digital twins and modelling bring transformative benefits to society and the environment by providing more efficient ways to monitor, model and control complex engineering systems.
Energy Saving
Our solutions make use of low-order digital twins that require less computing power than existing alternatives.
Accuracy
Achieving state-of-the-art accuracy and superior robustness to sensor noise.
Safety & Reliability
Strengthening resilience to help engineering systems adapt to unexpected changes and challenges.
As a platform for research and development, digital twins allow for quicker and more affordable testing of innovative ideas, reducing the need for expensive physical prototypes.
- Luca Magri, Professor of Scientific Machine Learning
FREQUENTLY ASKED QUESTIONS
A real-time digital twin is a set of virtual information constructs that mimic the structure context and behaviour of a real system. It is dynamically updated with data from the physical twin. It is the bidirectional interaction between the virtual and physical that is central to the digital twin.
As a platform for research and development, digital twins allow for quicker and more affordable testing of innovative ideas, reducing the need for expensive physical prototypes. From optimizing data centre cooling to improving transportation systems and advancing renewable energy solutions like wind farms and hydrogen storage, the applications are far-reaching. By bridging the gap between theory and real-world application, digital twins are helping to create a more sustainable, safe, and efficient future for everyone.
Our research is able to bridge the gap between the digital and real worlds. Through collecting real data through scaled down situational experiments in controlled environments, coupled with our stat of the art scientific machine learning knowledge, we are able to invent programmes that allow machine processes to operate autonomously to the optimum efficiency level.
Ultimately if a process is running at its optimum efficiency, you will save time, energy and money. In the quest for a carbon neutral society efficiency is key. In each of the case studies the capabilities of our Digital Twin Software are showcased.