Simulation data
is your most
valuable — and most
unmanaged — asset.

The foundation of simulation intelligence is governance — ensuring that every simulation artifact is captured, versioned, searchable, and protected. Today, only a handful of organizations have fully implemented structured simulation data management. The rest store models on local drives, results on shared file servers, and reports in email. When an engineer leaves, their simulation knowledge leaves with them. Nexus automatically captures every artifact generated during a simulation study: input decks, mesh files, solver configurations, material property assignments, boundary conditions, load cases, convergence data, result fields, and post-processed outputs. Each artifact is versioned, linked to the CAD geometry revision it references, and indexed with automatically extracted metadata — solver type, physics domain, mesh density, element types, material models, convergence status, and key result metrics.

Modern product development requires multiple simulation solvers — structural FEA in one tool, CFD in another, electromagnetic analysis in a third. Managing data across these tools is the primary barrier to simulation scalability. Engineers waste hours converting file formats, tracking which solver version produced which results, and manually transferring boundary conditions between coupled analyses. Nexus orchestrates multi-solver workflows from a single workspace. Define a simulation study that chains a structural analysis in Abaqus to a thermal analysis in COMSOL to an aerodynamic analysis in OpenFOAM — with automated data transfer between stages, HPC job submission, and results capture at every step. No solver lock-in. No manual file management. Every result traced to its solver, its inputs, and its design context.

The highest-value use of simulation is not running a single analysis — it is systematically exploring the design space. A parametric sweep across wall thicknesses, material grades, and cooling configurations can reveal performance landscapes invisible to single-point analysis. But DOE studies generate enormous volumes of data — hundreds or thousands of solver runs — that are nearly impossible to manage without governance. Nexus automates DOE setup, execution, result collection, and statistical analysis within the governed PLM environment. Define parameters, ranges, and sampling strategies. Nexus generates the run matrix, submits jobs to HPC, collects results, builds response surfaces, and identifies optimal configurations — with every run versioned and traceable.

The question "does this design satisfy the requirement?" is traditionally answered by an engineer opening three applications and making a judgment call. The requirement lives in DOORS. The simulation result lives in Ansys. The approval lives in the PLM. Nexus connects all three automatically. Each simulation study is linked to the requirements it verifies. When a solver run completes, Nexus automatically extracts the relevant result metrics and evaluates them against the requirement acceptance criteria. The verification matrix updates in real time — showing green for satisfied requirements, yellow for marginal compliance (within 10% of limits), and red for failures. When a requirement changes or a design revision invalidates a previous simulation, affected verification records are automatically flagged for re-evaluation.

Generative design is revolutionizing how engineers conceive structures — but without PLM governance, generative outputs are ungovernable. An AI-driven topology optimization can produce thousands of design candidates in hours. Which one did you select? Why? What requirements did it satisfy? What manufacturing constraints were applied? Traditional generative tools produce outputs disconnected from the product lifecycle. Nexus integrates generative design directly into the Axiom knowledge graph: every design candidate, every constraint definition, every objective function, every Pareto trade-off is versioned and traceable. Engineers explore vast design spaces while maintaining full lifecycle control — connecting AI-generated geometry back to the requirements it targets, the simulations that validated it, and the manufacturing processes that will produce it.

A high-fidelity FEA model with 10 million degrees of freedom takes hours to solve. For design exploration, real-time optimization, and digital twin applications, you need models that run in milliseconds while preserving physics fidelity. Reduced-order models (ROMs) compress the essential behavior of a full simulation into a lightweight mathematical proxy. Nexus automates ROM generation: from a series of high-fidelity training runs, it constructs polynomial, kriging, or neural-network surrogates that predict system behavior across the parameter space in real time. These ROMs are first-class PLM artifacts — versioned, linked to the training data that produced them, and tagged with validity boundaries. When the underlying design changes beyond the ROM's training envelope, Nexus automatically flags it for regeneration.

The most mature digital twin implementations combine physics-based simulation with real-time operational data — creating hybrid twins that are grounded in first principles and calibrated by reality. But building these twins requires governed simulation data: the high-fidelity models that capture the physics, the reduced-order models that enable real-time execution, and the training data that validates prediction accuracy. Nexus provides the simulation foundation for digital twin programs. From the initial FEA/CFD models created during design, through the ROMs generated for real-time deployment, to the IoT data streams that calibrate the twin in operation — every artifact is governed in the Axiom knowledge graph. When the physical asset deviates from the twin's predictions, the system automatically traces back to the simulation assumptions that may need updating.

The greatest waste in simulation is not compute time — it is lost knowledge. An engineer runs 500 simulations over a product development program, discovers critical insights about thermal behavior under certain load combinations, documents conclusions in a final report, and moves to the next program. Three years later, a different engineer faces the same thermal challenge on a new product — and starts from scratch because the prior knowledge is buried in a shared drive. Nexus's Results Intelligence engine applies AI to the entire simulation knowledge base: identifying patterns across programs, surfacing prior studies relevant to current challenges, and building an institutional memory that grows more valuable with every simulation run. Natural language queries against the knowledge graph return relevant prior work: "Show me all thermal simulations where junction temperature exceeded 140°C and the solution involved modified heat sink geometry."