ProCue
Dr. Craig Brown EngD MEng MIET AMIChemE is a simulation and algorithms veteran with over 12 years of experience.
Recently, he was responsible for motion and cueing development for the McLaren-MTS Vehicle Dynamics Simulator. Subsequently, as a principal engineer at Toyota and founding member of Toyota Gazoo Racing Simulation UK, he was responsible for the development of an international portfolio of state-of-the-art motion simulators..
He helped found the influential modeling and simulation department at Worcester Bosch Thermotechnology whilst completing his thesis on the application of recurrent neural networks to engineering control problems. He was support lead at dSPACE UK, helping many well-known OEM’s achieve their objectives in the domain of real-time simulation.
Now, he is director of BrownSim, an engineering services provider whose mission is to help companies to develop and optimise their simulation & digital twin activities.
Digital Twins
Digital twins approximate a product or organisation in digital form. With such a mathematical model to hand, expensive-to-answer "what-ifs" can be resolved safely and cheaply.
Twins could be of something as simple as a domestic appliance, as complex as an chemical plant or as comprehensive as an entire organisation - enabling one to quantify the impact of organisational change.
Dr Brown has a track record of high impact simulation projects. For example, at Bosch Thermotechnology his work on renewables influenced technology roadmaps at a strategic level.
Motion Simulators
Motion simulators are the ultimate digital twin of a vehicle. But simulators present extra challenges: the need for real-time interactivity, safety, constraints handling, control and motion cueing algorithms.
Operations, processes and research strategy are also critical to extracting maximum value from a given platform.
Dr Brown has over 5 years experience developing and operating motion simulators for companies such as McLaren and Toyota, including merger and acquisition experience.
Analytics
Unlike many data science firms we prioritise a "scientifically informed" or "white box" approach to modelling, exploiting domain knowledge and maximising comprehensibility.
Only when this is exhausted are regression models considered, ranging from linear through "deep learning" approaches. Such approaches are especially suited when a model is needed quickly.
Drawing on experience analysing heterogenous, noisy and "awkward" data across multiple industries, we will maximise the value of your data gathering activities.
Supporting academic research
BrownSim is committed to academic research. Be part of the BrownSim community or help improve your favorite cueing software.
Keep in touch
| in/craigrobertbrown | |
| craig.brown@brownsim.io |