Reduced Order Models (ROMs): Faster Simulation Without Losing Accuracy
Simulation teams are under constant pressure to deliver results faster, explore more design alternatives, and support real‑time decision‑making, all without compromising accuracy. Reduced Order Model simulations (ROMs) have emerged as a powerful way to meet these demands, but they are often misunderstood.
Here, EDRMedeso specialists answer practical questions from engineers about when ROMs make sense, how they are created, and how they should be used alongside high‑fidelity simulations. This blog summarises the most relevant questions and answers with a focus on real engineering scenarios rather than theory.
Why use a ROM if high‑fidelity simulations are still required?
A common misconception is that ROMs replace high‑fidelity simulations entirely. In reality, ROMs are designed to complement detailed simulations — not replace them.
A ROM does not require training data from the entire design space. Instead, it is built using a limited but representative set of simulation results, typically chosen to cover the boundaries and key operating conditions of the problem. This might mean training a ROM on just a handful of carefully selected simulations rather than hundreds of full model runs.
Once created, the ROM can be used to:
- Rapidly evaluate thousands of design points
- Perform fast “what‑if” studies
- Support real‑time or near‑real‑time predictions
High‑fidelity simulations are then reserved for validating only the most promising designs, significantly reducing overall simulation effort and turnaround time.
How do ROMs support large design space exploration?
ROMs are particularly valuable when engineers need to explore large design spaces. For example, rather than running thousands of full simulations, a ROM trained on a small number of representative samples can evaluate thousands of virtual design variants in seconds.
This approach allows engineers to:
- Identify trends and sensitivities early
- Narrow down design candidates quickly
- Focus detailed simulations where they add the most value
Our webinar highlighted that this workflow is especially effective when design variations are incremental or follow clear parameter ranges. (watch here)
Can ROMs be created from ANSYS Mechanical data?
Yes. ROMs can be built using data generated in Ansys Mechanical, but they are not limited to a specific solver. The ROM builder only requires that the data is structured and readable; it does not need to know where the data originally came from.
This means ROMs can be created from:
- Mechanical simulations
- Other Ansys solvers
- Even experimental or test data, provided it is properly formatted
This flexibility makes ROMs easier to integrate into existing simulation workflows.
Where do AI‑based ROMs fit into this picture?
There is growing interest in AI‑based ROMs. While machine learning can be a powerful enabler, it is not automatically the best choice for every problem.
AI‑driven ROMs are most effective when:
- Historical simulation data already exists
- The model is difficult to parametrise traditionally
- There are repetitive simulation tasks with small variations
However, if generating new training data is expensive or time‑consuming, simpler ROM approaches may be more appropriate. The key message was to choose the modelling approach based on the engineering problem, not the technology trend.
Key takeaway
ROMs enable faster design exploration, real‑time insights, and more efficient use of simulation resources. When used correctly, they allow engineering teams to focus high‑fidelity simulations where they matter most: accelerating development without sacrificing confidence in results.
Watch the on-demand webinar ‘Accurate and Fast Simulations enabled by ROMs’
