Closing The Gap Between Theory and Reality

An engineering student was troubleshooting an RF transmitter when he noticed something strange. The system worked when he stood next to it. When he stepped a few feet away, performance changed. 

The design hadn’t changed. The calculations hadn’t changed. Yet the behavior was different.

Eventually, the source of the problem became clear. Fluorescent lighting in the room was introducing interference. Standing close to the circuit, the student’s body had been unintentionally acting as a shield. When he stepped away, the interference returned.  

I first heard this story from the engineering faculty at a major U.S. research university, and I’ve thought about it often because it captures something I’ve seen repeatedly throughout my career. Understanding the theory is only the beginning of understanding how a system behaves in practice. Closing that gap is an essential part of engineering. 

For most of my career, I’ve watched engineering teams tackle increasingly complex challenges. What has changed isn’t engineers’ capacity to learn. What has changed is the pace of technology cycles. Power architectures are evolving rapidly. AI infrastructure is expanding exponentially. New wide-bandgap semiconductor materials are reshaping designs that veteran engineers spent decades mastering.

In modern engineering, the challenge isn’t just access to information. The challenge is turning that information into practical experience quickly enough to keep pace with the market. 

To thrive in this environment, technology leaders have to treat the speed of learning as an operational priority. I’ve seen three recurring obstacles that slow the transition from knowledge to practical experience. 

The trap of perfect simulations

One of the biggest misconceptions I encounter is the belief that if we can model something accurately enough, we fully understand it. Simulation has become remarkably sophisticated, allowing us to catch flaws earlier than ever. But eventually, every design must face reality. 

I was recently discussing a project with engineers developing a battery energy storage system. The team had adopted gallium nitride technology to improve power conversion efficiency. Their simulations looked promising. Their calculations were sound. Then they built the physical prototype. 

Fast GaN switching exposed effects the models had not fully captured, including parasitic inductance and capacitance, ringing, ground bounce and gate-voltage stress.

High-bandwidth, real-time measurement made those effects visible and enabled the team to correlate physical behavior with the simulated design. The breakthrough was not better simulation alone, but the ability to measure, understand and close the gap between the model and the real system.

The fastest path to a solution required a faster physical feedback loop. 

Shifting toward just-in-time expertise

Many organizations still separate learning from work. Engineers complete training, review statistic documentation, and absorb theoretical information long before they are asked to apply it. Experience, however, accumulates differently than knowledge.

I was reminded of this while working with a university’s Formula SAE team as they transitioned their race car from an internal combustion engine to an electric drivetrain. The team understood the underlying physics of electric vehicles. What they were working through was the challenge of integrating and validating a new high-voltage traction inverter system under the pressure of an upcoming competition.

As the team integrated the drivetrain, interactions among the battery, pre-charge circuit, isolation monitoring, inverter and controls created behaviors that were difficult to diagnose from the models alone.

Practical guidance and differential measurement gave the team visibility into the physical system, helping them troubleshoot isolation issues, validate high-voltage sequencing and safely progress toward inverter operation. Simulation defined expected behavior; real-world measurement helped them understand and validate the system they had actually built.

People learn more effectively when they can connect knowledge to a real problem they genuinely care about solving. When application follows closely after instruction, they can put that learning to work immediately.

The myth of the steady-state expert 

Learning gaps aren’t limited to students or early-career engineers. Some of the most significant arise when experienced teams encounter technology that requires them to reconsider familiar approaches.

Several years ago, the power electronics industry began a major transition from traditional silicon-based designs to silicon carbide architectures. The engineers leading that work had deep expertise.

The instincts that served them well in silicon did not always translate directly to SiC. We saw this firsthand through our work with a silicon carbide device manufacturer supporting an automative engineering team’s transition toward its next-generation electric vehicle platform.

The team had extensive experience in power electronics. But SiC’s faster switching and demanding voltage transients changed what they needed from their measurements. Probe selection and setup became critical to observing the circuit without distorting the signals the engineers needed to understand.

To accelerate this transition, we partnered with the manufacturer to conduct hands-on, collaborative workshops that paired advanced SiC theory with real-world, optically isolated measurement techniques. We put the hardware directly in front of these veteran engineers, allowing them to see—in real time—how switching topologies behaved under extreme stress.

Nobody in that room was a beginner. They were learning where familiar approaches needed to change. The experience reinforced a lesson for leaders: as technology shifts, experienced teams need opportunities to learn and apply new approaches, too.

The speed of adaptation

When technology moves this fast, the traditional ways we get engineers up to speed can fall short. The teams navigating these shifts smoothly manage the speed of learning just like any other operational bottleneck.

They map technical training directly to the upcoming design phase so engineers can apply it while it is fresh. They ensure labs are equipped to measure and analyze the latest signals at the latest components quickly and accurately—because engineers who cannot see the nuances of a signal are working with incomplete information.

They use low-stakes prototyping windows to let teams find edge cases before a design freeze. And they help pass on engineering judgment by bringing experienced colleagues into the lab to develop test setups alongside the team.

Many important advances begin with a moment on the bench when a physical circuit behaves differently than the model predicts. Investigating that difference helps engineers turn what they know into something they can use.

As leaders, we should pay attention to how quickly our teams can turn knowledge into real-world capability. The organizations best prepared to adapt are those that help engineers close the gap between theory and practice throughout their careers, beginning in the university lab.