In the race to harness AI in engineering, it’s tempting to assume machine learning (ML) can be bolted onto existing CAE workflows and instantly deliver transformational results. The promise is compelling — predictive insights, generative design, and optimization at scale. But that promise often remains out of reach. Before AI can deliver real value for new problems, there's a deeper, more foundational problem that must be solved: reliable, scalable numerical simulation automation.
To train AI models or build predictive systems, we must first be able to generate clean, contextual, and traceable simulation data at scale. That’s not just about solvers and hardware — it’s about structured, reproducible pipelines, standardized data schemas, and orchestration across physics, design parameters, and computational environments.
At PhysicsX, we believe simulation automation is not a productivity tool, but rather a systems design challenge. And it sits at the heart of any scalable, intelligence-driven engineering capability. In this blog post, we explore the challenges with automating numerical simulation at scale, outline a template architecture to resolve them, and describe the capabilities we have been able to create with this in the Simulation Workbench module of the PhysicsX platform to create robust data sets for ML.
Why Numerical Simulation Needs Rethinking — Through the Lens of Automation
Most engineering organizations still rely heavily on manual simulation workflows, some of which are decades old. Even with access to sophisticated solvers and powerful computational resources, a typical process of running a simulation looks like this:
- Create or modify CAD models
- Import modified CAD geometries
- Repair and de-feature for meshing tolerances
- Generate, refine, and validate the mesh
- Apply physics-specific boundary condition sets per load case
- Configure solver inputs manually or via in-house scripts
- Launch jobs to an HPC cluster or local workstation
- Manual post-process via third-party & GUI-based tools or standalone parsers
- Extract performance metrics, export images, and correlate data externally — often in tools such as Excel
- Store summary on a network drive, or email results to a colleague
This cycle repeats for every variant, every load case, and every design iteration. And it’s largely bespoke, lacking standardization or reuse across teams and physics domains. This introduces systemic pain points:
- Domain-specific handshakes between geometry, meshing, setup, and results
- Inconsistent solver interfaces, file formats, and mesh representations
- Context loss between geometry creation, simulation, and downstream analysis
- Limited or no interoperability with data science or ML tools
- No unified data schema for parameter sweeps, DOE or optimization runs
As a result, most simulation data produced today remains locked in silos — unindexed, unstructured, and stripped of design or physics context. Meanwhile, the demands of modern engineering are rising fast, with a need for:
- Multi-physics models across transient, nonlinear, and coupled domains
- Optimization across thousands of design variants and systems (DoE, MDO, reliability, robust design)
- Integration of real-world sensor/test data for validation and hybrid learning
- Real-time or reduced-order model evaluations for digital twins
- Structured, repeatable solver outputs for AI model pipelines
Without automation, these goals remain aspirational, as the architecture simply cannot support them at scale.
Why Simulation Automation Is Technically Challenging
Despite strong incentives, implementing simulation automation remains a major technical undertaking. It’s not just a matter of writing better scripts, it's about reorganizing fragmented tools and workflows into connected, modular systems that can run at scale.
We see four fundamental challenges that we’ve categorized into four core areas:
Tool Heterogeneity and Integration Gaps
Most simulation environments rely on a combination of CAD systems, pre-processing tools, physics solvers, and custom post-processing scripts — each optimized for a specific domain, but rarely built to work seamlessly together. These tools often differ in:
- Data structures and model formats
- Interface methods (GUI vs API vs scripts)
- Units, conventions, and terminologies
- Versioning behaviour and run-time dependencies
As a result, even trivial automation, like running the same simulation across 50 design variants, is fraught with inconsistent file handling, parameter representations, and tool-specific configurations.
To automate robustly, every tool in the stack must be abstracted into interchangeable, scriptable components that speak a shared language, which means introducing strong API boundaries and standardized data translation layers.
Lack of Process Modularity and Workflow Orchestration
Most current pipelines reflect hand-crafted simulation logic — sequential, tailored setups with hard-coded job sequences and minimal error handling. These limitations become critical when scaling:
- No structured way to define workflows, such as Geometry → Mesh → Setup → Solve → Post-process → Extract KPIs
- Manual tracking of job dependencies, leading to version mismatches or data loss
- Complicated and error-prone parallel execution across variants or parametric sweeps
- Conditional logic (e.g., re-meshing if a certain quality threshold fails) typically isn’t supported at all
In effect, the simulation process becomes a chain of shell scripts or human actions, which don't scale to 1,000s of variants or integrate neatly with downstream tools like Master Data Online (MDO) platforms or AI model pipelines. Solving this requires an orchestration layer — a directed graph-style execution engine that can reason about steps, dependencies, and scale across HPC or cloud infrastructure.
Poor Data Structuring and Traceability
Simulation data is often stored as loosely organized files: folders filled with logs, surface plots, field data, and summary reports, often hand-labelled and tracked via spreadsheets or email chains. This leads to several systemic issues:
- No versioning or audit trail between geometry changes, mesh refinements, or simulation parameters and results
- No way to compare across design variants unless manually aligned
- Difficulty in extracting structured scalars, field metrics, or KPIs consistently across multiple runs
- No shared representation between different physics or solver outputs
In short, simulation data lacks semantic structure, which makes it hard to query. This is a blocker for collaboration, post-processing, quality control, and downstream tasks like correlation with test data. What’s missing is a unified simulation database — one where each run is fully described by structured metadata, connected directly to geometry, mesh, configuration, boundary conditions, and input parameters — and fully searchable at runtime.
Disconnection from ML & Surrogate Modelling Pipelines
Even when good simulation results exist, getting value from them in the AI context is challenging. Simulation outputs are typically unsuitable for model training out of the box. Common issues include:
- Unstructured output formats (e.g., raw result files, .vtk plots, proprietary binary dumps)
- Lack of labelling or classification (e.g., failure modes, performance thresholds)
- Inconsistent parameter mapping between simulation inputs and outputs
- No feature extraction or processing logic (turning fields into tensors or meaningful engineering features)
- No automated feedback loop from model uncertainty to simulation triggering
As a result, teams wanting to build AI models must do significant pre-processing and manual data cleaning, slowing AI integration and limiting operationalization. In an ideal system, simulation infrastructure produces natively learnable datasets: annotated, contextualized, and correlated with simulation input parameters and physics constraints. This creates a seamless loop between high-fidelity solvers, structured data, model training, inference, and new simulation triggers (e.g., active learning, uncertainty-based refinement).
Building Simulation as a Scalable, Toolchain-Independent Data Engine
To enable ML in engineering design, simulation must evolve from a manual task into a structured, repeatable, and scalable data engine, producing high-fidelity, learnable datasets from physics-based models. This requires re-architecting simulation workflows with:
- Modular execution logic across pre-processing, solving, and post-processing
- Toolchain-agnostic abstraction layers for interoperability
- Structured, traceable, and semantically-rich outputs
- Tight coupling with real-time analytics and ML pipelines
The result is an engineering-grade system where simulation and ML work in tandem to accelerate design-space exploration, reduce computation cost, and improve robustness.
Key Building Blocks for the Simulation Toolchain
Modern simulation requires more than isolated solver runs — it demands an integrated, scalable, and intelligent workflow. The need of the hour is a “structured simulation toolchain architecture”. This architecture defines five core components that enable simulation at scale: 1) a unified data backbone for structured, versioned data; 2) interoperability for tool abstraction; 3) an orchestration engine for multi-step automation; 4) data transformation for insight-ready outputs; and 5) an intelligence layer for AI-assisted decisions. Together, they form a robust foundation for high-fidelity, ML-ready simulation pipelines.
Unified Data Backbone
A structured and version-controlled data layer that stores and manages all inputs, outputs, metadata, and relationships for every simulation run.
This backbone goes beyond a data repository, creating a semantic layer that renders the simulation and test data usable by downstream systems (i.e., automation, ML, reporting). Every geometry file, material card, boundary condition, solver setting, and result output is tagged, versioned, and linked to a project, variant, and context. It transforms traditional project folders and loose files into a cloud-queryable, ML-friendly knowledge system.
Key capabilities:
- Central hub for simulation, test rigs, and sensor data
- Relational mapping of design variables, solver IDs, workflow stage, time/version tags
- Schema aware of physical relationships (e.g., load cases, mesh refinement level)
- Enables search, audit, comparison, and traceability at any scale
Why it's essential: Without a clean, organized, and structured backbone, any downstream automation or AI is crippled. This layer makes simulation data smart, contextual, and connected.
Interoperability & Integration
The abstraction layer that connects multiple solvers, physics tools, formats, and environments into a unified framework.
This layer sits between the data structures, geometry, and physics solver execution engines. It provides standardized APIs and wrappers for various vendor tools, allowing them to participate in simulations without bespoke scripting per tool. It handles mesh conversion, solver input writing, boundary condition translation, and post-processor normalization, so that engineers aren’t reinventing interfaces every time a tool or version changes.
Key capabilities:
- Plug-and-play templates for common CAD tools and physics solvers
- Open and powerful framework to quickly create custom interfaces to other software, like in-house solvers or design tools
- Format normalization (e.g., native mesh vs universal mesh trees)
- Execution connectors to HPC clusters, cloud queues, or hybrid infrastructure
- Vendor-agnostic abstraction to future-proof process definitions
Why it’s essential: Simulation environments are rarely homogeneous. This layer turns diverse tools into interoperable units, enabling automation, orchestration, and AI to work across physics, not just within one domain.
Simulation Orchestration Engine
The logic engine that manages multi-step workflows, parallel simulations, design loops, and simulation dependencies — at scale.
This block handles all control logic for simulation execution: reading configurations, managing queues, launching solver jobs, retrying failed runs, resolving inter-step dependencies, and coordinating conditional workflows.
It allows simulation tasks to be defined modularly (geometry → mesh → solve → post-process → export) and executed natively or across a distributed infrastructure. Design-of-Experiments (DOE), optimization, transient simulation branching, and uncertainty propagation are all handled by this orchestrator.
Key capabilities:
- Scalable run orchestration (hundreds to thousands of variants)
- Run grouping, hierarchy, and naming
Why it’s essential: Scaling simulations isn’t just about running more; it’s about coordinating more. This engine choreographs thousands of logical steps across hundreds of runs so results remain aligned, interpretable, and actionable.
Data Transformation & Enrichment
Transforms raw solver outputs (and corresponding measurements, if available) into structured, enriched datasets ready for analytics, AI, or correlation studies.
It parses output logs, field results, surface and volume fields, and images from solvers and maps them onto structured representations.
Key capabilities:
- Extracts scalars, tensors, curves, discontinuities, and graphs
- Labels features and outputs
- Streamlines output across simulation runs
- Extracts field data and provides a unified data structure for AI
Why it’s essential: You can’t derive insight from raw solver dumps. This layer turns output into insight-friendly formats and makes it indexable, learnable, and cross-comparable across variants.
Intelligence & Learning Framework
Provides model-assisted decision making and AI-in-the-loop operations, including AI model feedback, active learning, retraining triggers, and uncertainty estimation.
This is the intelligence layer at the heart of AI-in-the-loop simulation. It maintains a catalog of ML models and can dynamically decide when to train, retrain, skip simulation steps, or trigger design-space refinements based on confidence thresholds. This layer also allows the simulation outputs to be connected with the AI models for initial training and model development.
It works as an intermediate agent, gating which simulations need to be run, and which can be replaced by model predictions with tolerable uncertainty.
What it provides:
- Initial feed into model development from simulation data
- Adaptive simulation logic (only run when needed)
- Continuous AI model learning pipeline
- Integration with design optimization tools
- Real-time simulation-performance feedback signals
Simulation Workbench
Our platform's Simulation Workbench module provides a powerful set of capabilities designed for scalability, repeatability, and intelligence. From automated workflows and parameterized run management to integrated multi-physics tools and AI-ready data pipelines, these features enable engineering teams to eliminate manual overhead, accelerate design cycles, and lay the critical groundwork for true data-driven simulation.
Workflow automation: Simulations are decomposed into modular steps — geometry, meshing, setup, solver execution, clean-up, and results extraction — each with configurable logic. This allows teams to apply consistent workflows across thousands of runs, projects, or teams.
Parameterized run management: Here, the user can easily define parametric sweeps, DOE, or optimization configurations. The system handles execution scheduling, dependency tracking, and error recovery across runs.
Data logging & metadata tracking: Every simulation is fully traceable, from parameter inputs to solver version and post-processing logic. All data is tagged, versioned, and queryable using structured metadata.
Multi-physics and tool integration: The Simulation & Data Workbench supports interoperability across various CAE platforms (e.g., Siemens, Ansys, Dassault, etc.), enabling simulation teams to aggregate and standardize workflows across tools and physics domains.
CAE-in-the-loop: The module’s advanced automation & integration capabilities unlock fully autonomous AI model retraining, where necessary, based on accuracy requirements. This means engineers no longer need to worry about model accuracy or uncertainties.
Data unification: While CAE vs real-world correlation is usually a major challenge, our AI models can learn from both CAE & measurement data, creating the “ultimate” model. The Simulation & Data Workbench is designed to unify and organize both simulation and corresponding measurement data.
Integrated data analysis and AI data preparation: Creating, organizing, and preparing CAE data for AI model training is a complex and often time-consuming task, typically far more demanding than the training process itself. The Simulation & Data Workbench streamlines this end-to-end workflow through purpose-built AI data preparation pipelines and advanced data analysis, eliminating manual overhead and accelerating readiness for ML.
Engineering agents helping you out: Specialized engineering agents, each trained for specific tasks within the engineering process, can assist with both repetitive operations and complex challenges. By decomposing larger problems into smaller, manageable subtasks, these agents collaborate to efficiently execute workflows that would otherwise demand significant manual effort.
What does all of this mean in practice?
- Massive acceleration in setup-to-insight loop times
- No more repetitive manual setup for every design iteration
- Configurable automation across physics disciplines
- A searchable archive of structured, reusable simulation datasets
- And perhaps most importantly, it creates the data backbone for AI
Automation = AI Enablement
So what does simulation automation have to do with AI? AI and ML are inherently data-driven. They excel when trained on larger quantities of consistent, well-structured data, which is practically impossible to generate through manual CAE workflows. But it’s exactly what simulation automation — when done right — provides.
The Simulation Workbench module of the PhysicsX platform solves this by transforming disconnected simulations into composable, structured data pipelines, paving the way for:
- High-fidelity AI modeling
- AI-powered design space navigation
- Active learning loops
- Foundation model development
- Anomaly detection in real-time runs
- Reinforcement learning for design optimization
With a robust automation layer, AI moves from prototype to production. And simulation becomes not just a tool, but an engine for continuous learning and innovation. Ultimately, simulation automation is the infrastructure AI needs. At PhysicsX, we’re building that infrastructure, enabling engineers can design faster, smarter, and with more confidence than ever before.