Generative Engineering
Abstract
Leveraging AI and modern compute to rebuild simulation workflows for mechanical and fluid dynamic engineering, resulting in drastically faster design throughput, iteration cycle and lower cost.
Description
For the past two years I’ve been actively hunting for investments within the engineering simulation workflow, most notably in computational fluid dynamics (CFD), but also in several mechanistic disciplines within robotics. For decades these fields have been dominated by extremely robust, mathematically defined solvers that prove out performance properties (aerodynamics, shear pressure, thermal exchange, etc.) with near certainty. New capabilities and cloud-based collaborative design have been folded into incumbent systems, but they remain largely static and now reflect a Frankenstein’s monster of acquisitions. Because these systems are deterministic, the computational load that most runs require is enormous. I commonly hear claims of simulations lasting a full week. And that’s hoping nothing crashes midway through. This inherent inefficiency, compounded across multiple sub-disciplines, leads to huge amounts of idle time and months-long development cycles that grind the entire innovation process to a crawl.
Mechanical engineering, like so many physical disciplines, has seen a talent flight over the past few decades, leaving the West with a precarious labor shortage at the worst possible time. Globally there are approximately 325,000 specialist engineers in this field, with approximately 50% of them over 50 years old. By some estimates, we’re currently 18% below where the market needs to be, and this will only get worse without serious training investment. Or maybe AI?
With modern AI and efficient GPU stacks, it’s now possible to generate near-real-time probabilistic simulations in a fraction of the time. By reducing a run from days to hours (or less), engineering teams are now able to massively accelerate design sprint cycles and capture that benefit through any combination of better products, faster shipping, or lower headcount.
Actually, this has been possible for a few years now. Despite the obvious advantages of ML-defined simulation and the generalized hatred of incumbent simulation tools, adoption has been astonishingly slow. Probabilistic “surrogate” models are not mathematically deterministic, and when dealing with planes and cars, small engineering errors can cost lives. “The stakes are too high,” as the argument goes, and while that is understandable, decades of that mindset have compounded into an extremely risk-averse culture where any deviation from the norm is just not worth it.
Working Thoughts
A simplistic value chain would be CAD → Meshing → Simulation / Solver → Product Lifecycle Management (PLM), with every component of the stack up for grabs. I’m particularly interested in the meshing stage, which appears to be a form of black magic with a uniquely strong impact on downstream solver time.
But what about organizations that haven’t been around for decades? Over the past three years, an enormous amount of energy and capital has flowed into the macro themes of reshoring and rearmament, giving birth to a new generation of engineering companies. These modern startups are AI-native and unburdened by the incumbent systems of the past. It’s for this reason that I place a disproportionate weight on founders embedded within these burgeoning new players (e.g. SpaceX, Hadrian, Helsing) rather than the typical talent pools of Airbus and Formula 1.
The migration from exquisite to attritable systems across defense organizations is also a major driver. The cost of failure is massively lower.
You can’t sell to an ostrich with its head in the sand. For sectors that haven’t taken the hint on modern systems, you might be better off going vertical to force the industry to change—or just take it all for yourself. Being the best heat-exchange simulation tool is likely not venture scale.
Relevant Companies
PhysicsX
Luminary Cloud
Nominal
Neural Concept
BeyondMath
Kernel
Revel
Flex Compute
Incumbents : Anysys, SimScale, Cadence, Autodesk, Synopsys, MathWorks