For as long as humans have been asking questions, we’ve also been trying to see what comes next. From reading tea leaves to studying the stars, the idea of knowing the future has held certain power and allure for millennia.
Simulations have long fascinated popular culture. In Greg Egan’s Permutation City, entire universes are computed from cellular automata and consciousness persists as software across infinite iterations, demonstrating the raw computational power to rewrite reality itself. And in Westworld, Bernard Lowe wields predictive engines to run millions of branching timelines, simulating every possible decision to anticipate and shape better outcomes.
But what if that power of foresight wasn’t pure fiction. Because you might not realise it, but advances in AI – specifically AI-accelerated simulation – are creating something that looks uncannily like it.
Simulating fiction – until now
Traditionally, if we were to take what Bernard Lowe does in Westworld – running millions of branching simulations to explore possible outcomes – and attempt to replicate it with real-world computational resources, it would require massive infrastructure and could take days, weeks, or even months.
The only way to know a simulation’s outcome was to run it. This high-fidelity modeling has long been essential in fields like climate science, manufacturing, drug development, and particle physics. Yet these simulations often demand vast computational resources, consuming time and energy on massive high-performance computing (HPC) clusters before generating a single usable result.
High-fidelity modelling at ENEA’s new CRESCO8 supercomputer illustrates just how demanding modern science has become. Before any physical test is run inside a fusion research environment, ENEA researchers use CRESCO8 to generate detailed simulations of how superheated plasma will behave under different magnetic confinement conditions. These models predict everything from turbulence patterns to plasma–wall interactions, allowing scientists to compare predicted behaviour with real-world test results and refine reactor designs far more safely and efficiently. Until recently, running a single high-resolution plasma simulation could take many hours of computation across thousands of parallel cores, consuming significant energy simply to model a few milliseconds of physical reality.
This challenge is particularly relevant in the UAE, where ambitious projects like the UAE Space Agency’s Mars Mission, Hope Probe, must balance complex simulations of space missions within stringent timelines. Similarly, efforts to develop advanced renewable energy systems, such as those at the Mohammed bin Rashid Al Maktoum Solar Park, rely on simulation to predict energy outputs, optimise infrastructure, and improve efficiency.
A fundamental shift in scientific capability
With CRESCO8, ENEA can now integrate AI-accelerated modelling techniques directly into its fusion research workflow. Lightweight neural models trained on traditional physics-based simulations can predict plasma behaviour or turbulence evolution almost instantly, acting as “AI solvers” that replicate the end state of large-scale simulations without the heavy computational load. Tasks that once demanded sprawling HPC runs can now be executed on a fraction of the hardware, often on a single GPU, radically reducing both energy consumption and turnaround time while preserving scientific accuracy.
This shift is particularly transformative in the UAE, especially in the case for urban planning and infrastructure. The modelling of Dubai’s smart city projects, such as real-time traffic optimisation or autonomous vehicle route planning, increasingly relies on AI-enabled simulations to anticipate patterns and outcomes. In critical industries such as energy, AI solvers are being evaluated for their potential to optimise the operation of electricity grids—key to achieving the UAE’s ambitious Net Zero by 2050 targets.
This methodology is already proving transformative in global science. At CERN, for instance, generative adversarial models (GANs) are revolutionising particle-physics research by replicating collision-event images that once required vast HPC clusters in a matter of seconds. The impact is remarkable: processes that were scientific bottlenecks are now becoming real-time tools, resulting in faster experimentation, cleaner energy use, and a new era of agility
Modeling entire galaxies in weeks
This transformation is not limited to particle physics and the field of nuclear fusion, with astrophysics researchers now experiencing similar leaps in simulation speed and fidelity thanks to advancements in HPC infrastructure.
Modeling cosmic evolution requires tracking processes that unfold across staggering timescales. A star takes several hundreds of thousands of years to form, and galaxies can take several tens of millions of years to revolve around themselves. But, as Dr Ana Duarte Cabral, a Royal Society University Research Fellow working out of the Cardiff Hub for Astrophysics Research and Technology says, we are now able “to create a model of an entire galaxy, tracking the formation and death of generations of stars, in only a matter of weeks. Before, the simulations took more than three times longer to run.”
In the UAE, where the International Astronomy Centre serves as a regional hub for space science and research, similar techniques are being adopted to simulate celestial events and inform space exploration strategies. These advancements offer the nation an incredible edge in bridging Earth-based challenges with space-based innovation.
At Cardiff University, improved systems driven by cutting-edge server architectures have doubled the available compute capacity and exceeded initial performance benchmarks by 46%. This allows researchers to process gravitational wave detection events and share data with the global astronomy community significantly faster.
The result is another form of ‘future-seeing’: an ability to explore cosmic evolution at a pace that is no longer measured in months or years, but in weeks. Researchers can test hypotheses about stellar formation, black hole interactions, and galactic dynamics with a level of speed and scale that was once inconceivable.
The implications of these developments extend far beyond efficiency. When a simulation takes 24 hours, researchers might explore a handful of scenarios. When it takes seconds – or when an entire galaxy can be modeled in weeks instead of months – they can explore thousands. That ability to examine millions of possible outcomes before conducting a real-world experiment gives researchers something uncanny: a way to compute the future rather than wait for it.
From the cosmos, back down to Earth
While cosmic modeling may feel distant from everyday experience, the same AI-accelerated approaches are entering domains that shape our daily lives too.
- In healthcare, the UAE’s efforts to become a global hub for medical innovation are increasingly dependent on AI solvers that accelerate molecular modelling, expediting breakthroughs in precision medicine. Such tools have proven critical in vaccine development and research on diseases with regional prevalence, such as diabetes and cardiovascular conditions.
- In transportation, Dubai’s Roads and Transport Authority (RTA) leverages AI simulations to train autonomous systems, preparing them to handle the complexities of urban environments and extreme weather conditions unique to the UAE.
- In manufacturing, AI-accelerated simulations allow Emirati firms in logistics and aerospace industries to optimise prototypes and reduce wastage, in line with the country’s sustainability agenda.
Not magic – but is this the closest we’ve come?
We still can’t literally predict the future. But we are entering an era where scientists and industries can compute the most probable future, with unprecedented speed and accuracy.
AI solvers don’t replace HPC, they elevate it. They compress the wisdom of countless simulations into models capable of delivering answers in seconds. For the UAE, which is rapidly advancing in sectors from AI and space exploration to smart cities and healthcare, this high-performance computing revolution offers unparalleled opportunities to design, test, and implement solutions that anticipate tomorrow’s challenges.
It isn’t magic, but it might be the closest we’ve come yet.
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