While an analyst would build finite element models for precise assessment, low- fidelity models are designer oriented. A low- fidelity (LF) or lumped- parameter model is an abridged scalar equivalent of a physical system, good enough to derive key characteristics such as normal modes and frequencies. LF models, composed of stiffness and inertial parameters, are best suited for fast estimates of performance for systems such as gear trains, mechanisms or rotor- shaft assemblies. LF models are easy to scale and enable faster optimization for boosting modal frequencies and such. In representing the real world system, LF modeling however does require judgement on part of the expert.

As founder of Dsgn2Last, I'm not a big fan of LLMs and inferential AI. Hundreds of billions of tokens are being used today to train some of the largest LLMs like Grok. Yet, LLMs are quite well known to give wrong answers or worse still, just intelligently frame ones that appear right when they aren't! Meanwhile, it's been estimated that in a decade or so, going by current trends, nearly 80% of the world's energy would serve to train LLMs. So much that former LLM proponents themselves are rethinking! What does all that sound like?

I believe that rule- based fuzzy intelligent systems (FIS) are phoenixes set to rise from the ashes. And (theoretically), FIS carbon footprints would be a nano fraction of their counterparts for LLMs. That's because unlike an LLM's massive training seeking to (hopefully) discover the needle of knowledge deeply buried in a mountain of information, FIS directly leverage that very knowledge through rules, antecedents, consequents and inference. This is a lot more respectful of the vast expertise that millions of engineers, economists and medical professionals have across their respective domains!

In my experience, FIS has been highly rewarding across very distinct real- world industrial and commercial problems. This is so when the FIS parameters are optimized to bring about a strong match with norms of real- world data metrics. 

Evolutionary design optimization has been one of my strong areas for well over two decades. While optimization in general is best under multi- disciplinary considerations, that could be challenging if not impossible for real- world situations. Most often, enough good can be realized from optimization for mechanical and/ or structural considerations and no more. Our services include everything from short- term evolutionary optimization solutions for new design concepts to re- design of existing ones, going upto development of customized applications for optimization, a long- term effort though.

Caveat: Optimization solutions are not guaranteed to always yield impressive gains. The search's extent of success is driven by factors such as constraint(s), signal- to- noise character of the objective being extremized and such.