How can Monte Carlo Simulations benefit the Defence sector?
In any defence project, there will be factors beyond your control.
However, thanks to modern advancements in Artificial Intelligence (AI) and Machine Learning (ML), project leaders are now more able to manage these variables. ML algorithms can process extensive data, making accurate predictions and identifying patterns that humans couldn’t discern without tech-based assistance.
One such class of algorithms are Monte Carlo Simulations, which can be used to model the probability of many possible outcomes in uncertain situations. By running the same scenario constantly and repeatedly with completely randomised variables, this method can be used to generate data-backed predictions of potential results. Above all else, it can aid understanding of the impacts of risk and uncertainty on outcomes. This can prove invaluable to decision and policy-makers, by presenting them with concrete probabilities rather than estimations.
In defence, these simulations can be used to evaluate scenarios it would be unfeasible to test in practice; such as flight trajectory and the endurance of defensive structures. Many different outcomes, from landing times to building survival rates, can be outputted with probabilities. When run in reverse this method can also help derive performance objectives; if desired outcomes are inputted, the most effective way to reach these goals will be returned.
Moreover, they can be useful in preventing projects from running overbudget or facing delays, which happens often in the Defence industry. Keeping projects on time and on budget helps retain faith from key stakeholders and the public, as well as mitigating future budgeting issues.
Such applications of ML require the right hardware to maximise their capacity. Most importantly, powerful GPUs are needed to process data quickly. The faster and more powerful the GPUs, and the more units working in conjunction, the more efficient data processing will be. A high-performance CPU, sufficient system memory and storage, and high-bandwidth network adapter are also required to keep things running smoothly and prevent bottlenecks in data transfer.
Weak or unreliable hardware can form another roadblock in delivering projects on time and on budget. That’s why we’ve developed a server for running Monte Carlo Simulations. From 8 NVIDIA L40 GPUs to a pair of cutting-edge AMD EPYC Milan 7713 CPUs, and a whopping 1TB of RAM, our server was designed to support even the most ambitious of ML projects.
Find out more about our specially-designed Monte Carlo Simulation and Machine Learning server here.
The UK Government have been keen to encourage innovation in Defence recently, allocating an extra £3.5 billion to promote technological and scientific advancements in the sector. On top of this, competitions are also being used to incentivise improvements in Human Machine Interaction (HMI), such as the recent Defence and Security Accelerator (DASA) competition, in which defence companies were encouraged to share a ‘Wizard of Oz’ demonstration of an AI-assisted project in a bid for funding.
Using this funding, Monte Carlo Simulations could bring companies further into the future of defence. Not only can they offer valuable foresight on unpredictable projects, investing in emerging AI technologies could help the industry stay ahead of the curve.