Simr Data Platform
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Developed by Simr, the Simr Data Platform is engineering data infrastructure that transforms proprietary simulation and physical-test outputs into structured, queryable, versioned, and ML-ready datasets while maintaining a native digital thread back to the originating simulation or test. Its core Semantic Data Layer converts proprietary outputs into an open, standardized data layer and attaches engineering meaning, including what a result represents, which inputs produced it, and the context in which it is valid. Built for both simulation engineers and data scientists, the platform provides visual curation through a Visual Query Editor, automated drag-and-drop data pipelines, versioning, provenance, and API access for downstream analytics, machine learning, and Physics AI. The platform is the engineering data operations layer of SimOps, sitting between simulation and physical testing on one side and analytics, ML, and Physics AI on the other.
Engineering organizations generate massive volumes of data from simulations, lab tests, and real-world feedback, yet much of its value remains untapped due to fragmented storage, incompatible formats, missing metadata, and fragile, non-scalable pipelines. To address this, the SimOps Team recently added 10 major best practices for engineering data (link: https://www.simops.com/simops-best-practices) to the framework, distilled from industry experience and insights from 232 engineering simulation projects. Unlike the other members of the SimOps Software Stack, which are assessed against the 20 SimOps best practices, the Simr Data Platform is assessed against these 10 SimOps Best Practices for Engineering Data, because its purpose is precisely the operationalization of engineering data. Together with the Simr team, we compared major features and capabilities of the Simr Data Platform with the SimOps Data best practices.
What the Simr Data Platform enables in practice
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Convert proprietary simulation and test outputs into structured, queryable, ML-ready datasets at the source
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Attach engineering meaning to results through the Semantic Data Layer, including inputs, context, and validity
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Build automated data pipelines through a drag-and-drop interface without programming
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Select surfaces, regions, and time steps visually with the Visual Query Editor, without scripting
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Version datasets and pipelines with native digital-thread provenance back to the originating run or test
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Serve simulation engineers and data scientists from the same curated data source for analytics and Physics AI
What the Simr Data Platform does not do
While the platform provides a strong foundation for engineering data operations, full SimOps maturity requires complementary capabilities, including:
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Simulation execution, self-service HPC access, and workflow orchestration, which are addressed by the separately assessed Simr Engineering Simulation Platform
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SPDM functions managing simulation artifacts and processes; the Data Platform operationalizes engineering data for analytics and AI rather than replacing SPDM
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A comprehensive, generalized automated data-quality rule framework covering all failure modes such as sensor drift and setup bias
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Full production-scale operational monitoring and failure-management mechanisms
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Organizational data-governance policies and enterprise data strategy
The platform is designed to integrate into this broader ecosystem through its API access and its deployment inside the customer's cloud or on-premises infrastructure.
Why the Simr Data Platform is SimOps Compliant
In the following, we check the SimOps compliance of the Simr Data Platform against the 10 SimOps Best Practices for Engineering Data. The platform is, at its core, an engineering simulation and test data operations platform: it captures data at the source, structures it through the Semantic Data Layer, curates it visually, versions it, and connects it back to its origins through a native digital thread. Its central proposition is Capture, Structure, Version, Connect. Simulation engineers retain control over engineering meaning and curation, while data scientists receive structured, labeled, and traceable datasets suitable for model development. With the capabilities below, the platform directly addresses the principal barriers SimOps identifies to the effective reuse of engineering simulation and physical-test data.
1. Convert Simulation Data Immediately into Analytics-Friendly Formats
This is essentially the core purpose of the platform. It converts proprietary simulation and test outputs into an open, standardized data layer, producing structured datasets that can be queried and consumed by engineers and AI systems. Data is captured at the source rather than requiring engineers to return months later and manually extract historical results, and the platform maintains parsers for multiple simulation formats and disciplines rather than requiring every engineering team to develop its own extraction scripts. This is a direct implementation of the best practice.
SimOps Compliance Level: High
2. Centralize Data in a Shared, Structured Hub
The platform directly addresses the problem of engineering data scattered across HPC scratch, file shares, laptops, and different simulation and test environments. It creates structured, queryable datasets with a shared engineering vocabulary, enabling searches and filtering across projects, and serves both engineers and data scientists from the same underlying data source, with API access for downstream AI and analytics. The Semantic Data Layer interprets engineering data and makes it comparable and queryable, which is substantially closer to the SimOps concept of a structured engineering data hub than a conventional data lake.
SimOps Compliance Level: High
3. Preserve Metadata as Carefully as Results
This may be one of the platform's strongest areas. The Semantic Data Layer attaches engineering meaning to results, including what the result represents, the inputs that produced it, and the context under which it is valid. The platform also maintains a native digital thread linking data to geometry, solver, settings, design iteration, inputs, workflow, pipeline, pipeline version, and the originating job or run. In Simr's own words, the provenance is native, not bolted on. This is essentially a textbook implementation of the best practice.
SimOps Compliance Level: High
4. Filter and Slice Data at the Source
The Visual Query Editor lets engineers select the relevant parts of a simulation result visually, including surfaces, regions, time steps, and sections, with no scripting required; the platform performs the extraction. The resulting dataset contains the selected engineering variables and results rather than reproducing the entire raw solver output. This combines engineering judgment with automated extraction, exactly as the best practice intends.
SimOps Compliance Level: High
5. Build Repeatable Automated Data Pipelines, Not Manual Curation
Engineers build pipelines themselves through a drag-and-drop interface without programming. A typical pipeline extracts source data, attaches engineering context, normalizes the information, generates features, and assembles the dataset, and once created it automatically processes new files as they arrive. Parsing and analysis of simulation result files is automated rather than manually set up for every new file. This is not merely theoretical: in an automotive deployment, crash-simulation data flows through the pipeline and into the customer's AI models without manual handling at every stage.
SimOps Compliance Level: High
6. Detect Subtle Data Quality Issues, Not Just Obvious Errors
The platform provides several important foundations: engineers curate and validate data, 3D inspection is available, anomaly detection is supported, and automation handles repetitive scanning and comparison, with prepared datasets described as curated, labeled, accurate, and contextual. Engineers remain responsible for validating correctness before downstream use. The publicly available documentation does not yet provide sufficient detail to confirm a comprehensive, generalized automated data-quality rule framework covering every failure mode named by the best practice, such as sensor drift, setup bias, and corrupted metadata.
SimOps Compliance Level: High
7. Design Pipelines for Scale from Day One
The platform provides strong scale indicators: automated processing of new files, reusable pipeline definitions, versioning, structured datasets, API access, support for many solver formats across multiple engineering disciplines, and deployment inside customer cloud or on-premises infrastructure. It has been used across different domains, including acoustics, crash safety, thermal analysis, and physical-test data, showing that the architecture is not tied to a single solver or discipline. Detailed production-scale operational monitoring and failure-management mechanisms are not fully demonstrated in the public material.
SimOps Compliance Level: High
8. Replace One-Off Scripts with Versioned Services
Instead of engineers repeatedly writing solver-specific extraction scripts, the platform provides reusable data pipelines. Those pipelines are versioned, the resulting datasets retain their provenance, and the platform records which pipeline and which version of the pipeline definition processed the data. Dataset plus pipeline plus pipeline version plus source run yields reproducible engineering data lineage, which is exactly the reproducibility the best practice calls for.
SimOps Compliance Level: High
9. Add Visual Verification to Every Data Extraction Step
The platform is explicitly built around engineer-led visual interaction. The Visual Query Editor allows engineers to select portions of simulation results visually without scripting, and the platform can automatically generate 3D, 2D, and 1D visualizations, reports, and KPI tables. Most importantly, engineers remain in control of the validation process: they assign meaning, curate relevant data, and validate correctness before downstream use. This is very close to the intent of the best practice.
SimOps Compliance Level: High
10. Preserve the Physics, Don't Collapse Data into Single Metrics
This is perhaps the most intellectually important match between SimOps and the platform. Rather than extracting isolated values, it builds structured datasets linking design inputs, operating conditions, engineering results, and labels while preserving the relationship to the original simulation or test. It supports transient and steady-state analysis across multiple disciplines and can combine simulation predictions with corresponding physical-test measurements, retaining their differences for surrogate modeling, simulation calibration, anomaly detection, and digital twins. That is exactly the kind of physics-preserving data relationship the best practice is designed to protect.
SimOps Compliance Level: High
SimOps Compliance Summary
Number | Best Practice for Engineering Data | SimOps Compliance Level |
|---|---|---|
1 | Convert Simulation Data Immediately into Analytics-Friendly Formats | High |
2 | Centralize Data in a Shared, Structured Hub | High |
3 | Preserve Metadata as Carefully as Results | High |
4 | Filter and Slice Data at the Source | High |
5 | Build Repeatable Automated Data Pipelines, Not Manual Curation | High |
6 | Detect Subtle Data Quality Issues, Not Just Obvious Errors | High |
7 | Design Pipelines for Scale from Day One | High |
8 | Replace One-Off Scripts with Versioned Services | High |
9 | Add Visual Verification to Every Data Extraction Step | High |
10 | Preserve the Physics, Don't Collapse Data into Single Metrics | High |
The analysis above demonstrates the SimOps compliance of the Simr Data Platform. The platform reaches a High compliance level across all ten SimOps Best Practices for Engineering Data, from analytics-ready conversion, centralized structured data, and metadata preservation to automated pipelines, versioned services, visual verification, and physics preservation. In data quality detection and production-scale pipelines, the complete operational implementations described by the best practices are not fully demonstrated in the public material.
The Simr Data Platform is positioned as an engineering simulation and test data operations platform within the SimOps Software Stack, operationalizing engineering data for reuse, analytics, and Physics AI. Its central contribution is transforming proprietary and fragmented simulation and test outputs into contextualized, versioned, and traceable engineering datasets connected to their origins through a native digital thread. Together with the separately assessed Simr Engineering Simulation Platform, it forms a two-product Simr architecture within the SimOps Software Stack, treating engineering data as a structured, production-grade asset rather than a collection of files.
If you are interested in the 25-page extensive analysis report, send your request to info@simops.com.
