Modeling and Simulation in Support of Compliance Activities: Overview and Key Benefits

Across every industry, organizations face mounting pressure to deliver innovative, high-quality products while navigating increasingly complex regulatory requirements and industry standards. In this environment, modeling and simulation (also called M&S) is transforming how products are designed, tested and certified. When independently verified and validated, modeling and simulation can empower earlier insights, reduce the need for physical prototypes and support more consistent engineering judgments — helping manufacturers move faster and with greater confidence. But what does modeling and simulation mean in practice? Where can it add value, and where does physical testing remain essential?

What is modeling and simulation?

Modeling and simulation creates a digital representation of a real-world product, system, process or phenomenon and uses that model to explore behavior under different conditions. This approach, often referred to as virtual prototyping, allows teams to evaluate designs and predict performance before building physical prototypes. The U.S. Department of Defense (DoD)^[1] and National Aeronautics and Space Administration (NASA)^[2] define a model as a physical, mathematical or logical abstraction of reality, and simulation as the execution of that model over time to study its behavior. These definitions emphasize the importance of context, traceability and credibility in every simulation activity.

Digital twin technology

Digital twins take the principles of modeling and simulation a step further. While traditional modeling and simulation is often used during design and validation to explore “what if” scenarios, a digital twin — a dynamic, real-time digital counterpart of a physical product or system — extends this capability into the operational phase. By integrating live data from sensors and connected devices, digital twins continuously mirror the current state of the physical asset, enabling predictive insights, fleet-level monitoring, performance optimization and proactive maintenance strategies. This transforms static models into living systems that simulate and anticipate, helping organizations manage complexity and improve reliability throughout the entire product life cycle.

The importance of establishing model trustworthiness

Shrinking product development cycles, growing complexity and miniaturization, and escalating regulatory demands stretch budgets and laboratory schedules. Frequent design changes often lead to more prototypes and a higher risk of late-stage noncompliance and rework. Modeling and simulation helps companies address these challenges by providing earlier, deeper, repeatable insights. However, the value of a model depends on the trustworthiness of its predictions.

To support any decision, such as design trade-offs or proceeding with failure analysis or certification, models need explainable assumptions, traceable inputs and well-designed validation tests that quantify uncertainty against the physical reality they represent.

Verification and validation (V&V) — with the specific focus on the support of certification decisions — are central to this process and essential to demonstrating a model’s credibility.

Verification evaluates whether the computational model has been built correctly and solves the intended mathematical equation using the appropriate physics. This step establishes that the model structure and submodels are suitable for the phenomena being represented and helps prevent reliance on parameter adjustments that might fit existing data but fail under new conditions. The model can be used as a predictive tool if and only if it uses the correct physics.

Model validation, physically performed in a laboratory environment, evaluates how well the model reproduces real-world behavior by running it under the same conditions as physical tests and comparing the outputs to measured data. While tuning parameters can be adjusted to improve alignment, validation establishes that these adjustments remain within reasonable limits and do not compromise the model’s predictive capability. This process demonstrates that the model is not only technically accurate but also credible for its intended application.

Together, verification and validation can transform simulation into a tool that can support design optimization, compliance and certification decisions. Guidance and standards from the U.S. Food and Drug Administration (FDA),^[4] National Aeronautics and Space Administration (NASA),^[5],[6] and American Society of Mechanical Engineers (ASME)^[7] stress that the importance and rigor of V&V should be scaled to the risk and impact of the decisions being supported, with clear documentation of context, assumptions and limitations.

Key benefits of modeling and simulation

By integrating simulation into the development process, organizations can realize several important benefits. These include:

• Better understanding of complex systems – Simulation can help teams see how different parts of a product or system interact, even when those connections are hard to spot in real life.
• Finding problems earlier – Organizations can quickly test ideas and spot potential issues before investing in building physical prototypes or tools.
• Saving time and money – Using virtual models can help manufacturers decrease the number of physical prototypes needed, focus laboratory testing where it matters most, speed up development and lower costs.
• Making decisions with greater confidence – Clear study plans, sensitivity analyses and traceable comparatives can help teams across your organization stay aligned and make informed choices.
• Risk reduction through virtual testing – Testing designs virtually enables teams to identify and address risks sooner, making surprises such as costly late-stage changes less likely.

Types of modeling approaches

When evaluating products for compliance, modeling approaches typically fall into two broad categories: computational multi-physics models and statistical or machine learning models.

Computational multi-physics models are grounded in the laws of physics and use mathematical equations to simulate complex interactions such as heat transfer, fluid flow, mechanical stress and electromagnetic phenomena within a product or system. These models are built using specialized software and rely on accurate physical parameters and boundary conditions to reflect real-world behavior.

Statistical and machine learning models are data driven. They use historical data and advanced algorithms to uncover patterns, make predictions or classify outcomes. Rather than modeling the underlying physics directly, statistical and machine learning approaches learn relationships from data, making them especially valuable when physical mechanisms are too complex or not well characterized.

V&V is essential for both types of models, but the approach differs. For computational multi-physics models, verification establishes that the mathematical model is implemented correctly — checking that the software solves the equations as intended and that numerical errors are minimized. Validation then compares the model’s predictions to physical test data, confirming that the model accurately represents the system for its intended use. Uncertainty quantification is also critical, as these models depend on input parameters that may vary.

For statistical and machine learning models, verification focuses on correct algorithm implementation and data handling, while validation assesses predictive performance using independent datasets and statistical metrics. Here, uncertainty is often expressed through confidence intervals or robustness checks. Both modeling approaches can provide valuable supplemental evidence to support engineering judgment and compliance when models are verified and validated.

Model trustworthiness is established through a transparent model verification and validation process (MV&V) tied to a specific context of use and aligned to the physical tests the model is intended to inform.

Where modeling and simulation adds value and where physical testing remains necessary

To maximize the benefits of modeling and simulation, organizations must recognize where these digital tools deliver the most value and where physical testing remains indispensable. Modeling and simulation excels at thermal or temperature-rise studies, airflow analysis, mechanical response and performance envelope exploration — helping to predict hot spot behavior, optimize cooling effectiveness and identify worst-case configurations across product families. Design-space screening and sensitivity analyses further support engineering judgment, helping organizations reduce the number of prototypes and inform the design of experiments before laboratory testing so they can focus on physical testing where it matters most.

Some organizations benefit from a hybrid approach. For example, fire behavior and flammability can be explored through simulation to understand thermal gradients and enclosure effects, but conformance tests in applicable codes and standards are still required for compliance. Similarly, complex multi-physics scenarios with uncertain inputs use simulation to define the outer limits of potential risk and identify worst cases, but validation data and uncertainty quantification are needed for physical testing to be reduced.

Certain compliance activities remain firmly in the realm of physical testing. For example, codified conformance tests for electromagnetic compatibility (EMC), dielectric withstand, short-circuit and spacing, as well as long-term aging, environmental durability and variability under real manufacturing tolerances demand empirical data, because simulation alone cannot fully capture underlying degradation mechanisms or distributions.

Prioritizing tests and simulations to maximize impact

When determining where to begin, prioritize simulations by time and cost impact. Near-term focus may include thermal or temperature testing, mechanical (vibration), fire safety, noise emissions and flammable refrigerant leakage evaluations. Future priorities may include short-circuit withstand, dielectric strength, electromagnetic compatibility, component breakdown and spacing measurements. These activities are applicable to:

• Materials
• Components, such as connectors, switches, sensors, cables and power supplies
• End products, including medical devices, industrial systems, IT equipment, consumer goods and climate control
• Ecosystems, such as smart buildings, manufacturing environments and data center infrastructure

Across sectors, authorities increasingly accept validated models as supporting evidence — not wholesale replacements. That’s why UL Solutions explicitly integrates model verification and validation (MV&V) with traditional evaluations and positions digital models as a pathway to fewer, better-targeted physical tests.

From model development to compliance support: The MV&V process

After a customer develops the digital model, UL Solutions can perform MV&V against a defined context of use. We create a test plan aligned to intended standards and compare simulation results to test data generated in UL Solutions laboratories or approved external laboratories via the Data Acceptance Program (DAP). The outcome is an MV&V report documenting scope, datasets, acceptance criteria and uncertainty. Evaluators may reference this evidence in future certifications — potentially enabling customers to reuse validated submodels across product families or platforms, run simulations in parallel to explore design options more quickly, and focus laboratory testing on the most valuable areas. A principal engineer confirms that simulation is suitable, and evaluators determine how the model informs engineering judgment. Where standards do not yet permit simulation to replace testing, models supplement required tests rather than substitute for them.

How modeling and simulation fits in today’s certification workflow

UL Solutions evaluators may apply engineering judgment to determine whether prior data — and, by extension, validated simulation evidence — is sufficiently related and relevant to the proposed construction to reduce or waive selected tests. Integrating trusted models with traditional evaluation can help teams target the most value-adding tests.

Business impact: More efficient and confident design choices

Validated digital twins may shorten development time, reduce prototypes and deliver earlier engineering insights that help mitigate the risk of late-stage noncompliance. This can translate to faster time to market, lower test spending and more confident design choices.

Beyond speed and cost, MV&V can help improve consistency: When the same physics, boundaries and acceptance criteria established in a verified model are applied to similar designs, evaluators can make more uniform engineering judgments across a portfolio.

Digital model verification and validation in action

A milestone in digital certification was achieved in 2024 when UL Solutions granted Siemens the first certification using digital modeling and simulation for the SINAMICS G220 variable frequency drives.^[8] By leveraging a verified and validated digital twin, Siemens was able to conduct the critical temperature-rise test required by UL/IEC 61800-5-1, the Standard for Adjustable Speed Electrical Power Drive Systems — Part 5-1: Safety Requirements — Electrical, Thermal and Energy, through simulation, significantly reducing targeted physical evaluations.

Trustworthy models for scalable compliance

Transparent models with traceable inputs, documented updates and explicit limits of applicability build trust and enable reuse. MV&V artifacts such as scope, validation datasets, acceptance criteria and uncertainty analyses allow verified and validated models to be applied to adjacent variants within their context of use and scale through parallelization.

As modeling and simulation continues to reshape product development and compliance strategies, understanding how V&V build model trustworthiness is essential. To dive deeper into how modeling and simulation verification and validation can support your compliance goals, connect with our experts or visit our Modeling and Simulation Verification and Validation for Compliance page.

[1] U.S. Department of Defense. 2024. DoD Instruction 5000.61 DoD Modeling and Simulation Verification, Validation, and Accreditation. 
[2] National Aeronautics and Space Administration. 2024. NASA-STD-7009B: Standard for Models and Simulations. 
[3] American Society of Mechanical Engineers. 2016. Overview of ASME V&V 20-2009 Standard for Verification and Validation in Computational Fluid Mechanics and Heat Transfer. 
[4] U.S. Food and Drug Administration. 2023. Assessing the Credibility of Computational Modeling and Simulation in Medical Device Submissions: Guidance for Industry and Food and Drug Administration Staff. 
[5] National Aeronautics and Space Administration. 2024. NASA-STD-7009B: Standard for Models and Simulations. 
[6] National Aeronautics and Space Administration. 2019. NASA Handbook for Models and Simulations: Implementation Guide for NASA-STD-7009. 
[7] Stress Engineering Services Inc. 2022. V&V 40: Computational Modeling for Medical Devices. 
[8] UL Solutions. 2024. UL Solutions Grants Siemens First Certification Using Digital Modeling and Simulation.