AI and the Promise of Hardware Iteration at Software Speed

Hardware engineers have long relied on large-scale simulations to design everything from turbines to biomedical implants. These simulations, rooted in classical physics, are fundamental to ensuring that prototypes are resilient, efficient, and safe. But the process—often involving painstaking mesh generation and days of number-crunching—can stretch development timelines significantly.

A new wave of machine learning (ML) tools promises to drastically reduce these timelines, allowing for hardware iteration at something much closer to software speeds. By reimagining how simulations are performed, ML-based tools can both accelerate the design process and enable deeper, more creative engineering solutions.

Below, we explore the current state of hardware engineering, how ML can supercharge simulation, and the barriers that must still be overcome before we can iterate on physical products nearly as quickly as we can iterate on code.

The traditional engineering process

Engineering teams for physical products generally move through a cyclical process:

1. Conceptual design: Generate initial ideas based on functional requirements, cost targets, and customer needs.

2. Simulation & analysis: Use software (e.g., finite element analysis or computational fluid dynamics tools) to predict whether a design meets performance, safety, and regulatory criteria.

3. Prototype & testing: Build physical prototypes to confirm simulations and refine the design if needed.

4. Iterate & optimize: Gather insights from testing, update the design, and run more simulations.

   
   

Because these simulation tools run on large, complex codebases—often derived from half a century of legacy software—it can take days or weeks just to simulate a single design iteration. Although HPC clusters and cloud compute have somewhat eased the pain, fundamental improvements in simulation algorithms have lagged behind advances in computing hardware.

One key reason: the incumbents in simulation software have historically focused on incremental improvements and stability. Tools are typically closed-source, reliant on older programming languages, and optimized for CPU-centric architectures. Any attempt to drastically change the underlying solver technology has faced friction in an industry wary of unproven methods.

However, hardware teams desperately need the ability to test out multiple design variations quickly, long before building expensive prototypes. This is precisely where ML-based approaches are poised to disrupt the status quo.

ML-based approaches to simulation

ML models can be trained to learn the complex relationships between input variables (e.g., geometry, material properties, boundary conditions) and the resulting performance metrics (e.g., structural stresses, thermal gradients, or fluid flow patterns). The process can be broken down into two major steps:

1. Training (Offline):

   • Gather a wide range of design data and simulation results, typically from a company’s own historical simulations or from open datasets.

   • Train an ML model to map from initial and boundary conditions to predicted outcomes, effectively learning how the geometry and other parameters influence performance.

2. Inference (Runtime):

   • For a new design, the model instantly provides a prediction—often in seconds—by performing a forward pass. This avoids the iterative element-by-element calculations used in classical solvers.

   • As the model becomes more sophisticated, it can deliver higher-fidelity outputs, rivaling (and sometimes outperforming) legacy solvers.

     
     

By reducing days-long simulations to a near-instantaneous inference step, engineers can iterate on designs much more rapidly. A shift toward GPU and specialized AI hardware also promises additional speedups, as these platforms are tailored for matrix operations that lie at the heart of deep learning.

Drawing inspiration from weather forecasting

Similar transformations are happening in weather forecasting. Classical methods rely on massive HPC simulations of atmospheric equations. New AI-driven approaches can deliver comparable accuracy but at a fraction of the computational cost. This success illustrates how ML-based simulations, once dismissed as “black-box approximations,” can become integral to industries where reliability and speed are paramount.

Progress, adoption, and challenges

Despite the promise of ML-based simulation, industry adoption is still in early phases. A few factors contribute to the cautious pace:

1. Cultural inertia and risk aversion

   • Legacy tools are embedded in engineering workflows and taught at every level of academia. Dislodging them requires convincing teams to trust new methods—especially for safety-critical applications like aerospace or medical devices.

   • “If it isn’t broken, don’t fix it” is an apt description of how many engineering managers feel, particularly if their projects involve human passengers or large financial stakes.

2. Data accessibility

   • Quality training data—consisting of validated designs and simulation results—can be hard to obtain or expensive to generate.

   • Proprietary designs often cannot be shared across companies, limiting startups’ ability to build extensive, domain-specific datasets.

3. Technical complexity

   • Training and deploying ML models involves specialized skills that many mechanical or aerospace engineers lack.

   • Tools with user-friendly interfaces are only now emerging, and they must integrate seamlessly with existing engineering software to reduce friction.

4. Regulatory and standards compliance

   • Some industries, especially aerospace and automotive, demand rigorous validation before adopting new simulation approaches.

   • Standards organizations are starting to explore how AI-based simulation might be incorporated, but these efforts can move slowly.

     
     

Go-to-market strategies

Several nascent startups and research labs are exploring different ways to accelerate adoption of ML-based simulation. The most promising strategies focus on bridging the gap between AI research and real-world engineering needs:

1. Full-service, high-touch engagements

   • Early-stage companies embed technical teams directly with customers, providing hands-on help with data, training, and process integration.

   • This approach builds trust and showcases success stories that other engineering teams can see and replicate.

2. Education and community

   • Offer free or heavily discounted tools to university engineering programs, student competitions, and research labs.

   • Foster a generation of engineers who are first exposed to simulation via ML-based methods—and expect faster iteration cycles from day one.

3. Focus on early-phase design

   • Market these tools primarily for conceptual design and trade studies, where speed is paramount and perfect fidelity is not always necessary.

   • Once ingrained in the workflow at the early stages, the simulation platform can expand to cover more detailed analyses.

4. Targeting risk-tolerant segments

   • Sectors such as automotive R&D, robotics, and consumer electronics might adopt faster than, say, commercial aviation or medical devices.

   • Within large enterprises, approach advanced research teams first, where innovation is prized and bureaucratic roadblocks are fewer.

     
     

Hardware iteration at software speeds

The vision of “hardware iteration at software speed” is one in which physical products can be designed, simulated, prototyped, and refined in a fraction of the current time—unlocking new potential for innovation. ML-based simulations are only one pillar of that future, but they may be the most pivotal. By bringing simulation times down from days to seconds, they enable vastly more exploration and optimization.

Other changes will also be necessary: faster prototyping methods (e.g., advanced additive manufacturing), better supply chain coordination, and broader acceptance of AI-driven design workflows. But the foundation is being laid now. The new crop of simulation startups, coupled with open-source frameworks like NVIDIA Modulus, has begun turning engineers’ wishful thinking into tangible tools.

It’s still early days, and the path to full-scale industrial adoption is riddled with challenges—technical, cultural, and regulatory. Yet each year, more success stories emerge from teams using AI-based solvers to design better products faster. We are on the cusp of a major shift in how the world approaches hardware development, and it’s an exciting time to be an engineer or founder in this space.

If you’re a hardware or simulation engineer—whether you’re curious about ML-based simulations or already working on the cutting edge—please reach out. We’re always eager to learn from real-world practitioners and innovators propelling us toward a world where hardware iteration truly can keep pace with software.