Modernize Your Simulation Workflows

AI/ML in the Loop: Training and inference without the disk round-trip

Simulation teams increasingly want reduced-order models, neural operators, and other machine learning systems trained on high-fidelity runs. Instead of writing simulation data to disk and training offline, stream runtime data directly into AI training and inference workflows. Zero-copy, GPU-resident execution allows models to learn from every timestep the solver computes while avoiding the performance penalties of moving data between systems.

In our own pipelines, moving from a synchronous extract path to asynchronous execution reduced a representative workload from 9.14s to 0.65s while sustaining 80K+ steps per second on GPU. That is the difference between training that keeps pace with the solver and training that throttles it.

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Aerospace and CFD: Quantitative analysis at solver speed

External aerodynamics workflows depend on quantitative engineering analysis, not just visualization. Compute lift and drag integration, vortex extraction, streamlines, boundary-layer metrics, and frontal area measurements directly during simulation instead of relying on sparse output snapshots. By performing analysis in situ, teams can track force distributions and flow structures across every timestep, generating reproducible engineering results that support validation, reporting, and design decisions.

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Exascale Physics: When the data is too big to ever land

High-energy physics and other exascale simulations generate data at a volume where writing full timesteps to disk is no longer practical. In many cases, the simulation outruns the file system by orders of magnitude. Perform analysis and visualization where the data is created, on the same CPUs and GPUs the solver uses, or stage workloads in-transit onto dedicated resources when appropriate. Instead of deciding which few timesteps can be saved, teams can analyze the complete simulation as it runs and preserve only the results that matter.

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Coupled Multiphysics: Conduit as the in-core bridge

Many multiphysics applications require two or more solvers to exchange field data at every timestep. Instead of routing data through disk or maintaining bespoke coupling code, simulations can share a common in-memory representation of meshes and fields that keeps data synchronized across applications. Success depends on consistent schema, partition handling, and complete metadata across the coupled workflow. When those foundations are in place, teams can build scalable co-simulation environments that exchange data accurately and efficiently throughout the entire run.

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Digital Twins: A closed loop between model and asset

A digital twin must do more than visualize a physical asset. It must continuously compare live telemetry against a running model and adapt as conditions change. Operators can inspect the simulation while it runs, assimilate new sensor data, and steer the model in place to keep the twin aligned with the real-world system. By incorporating reduced-order and surrogate models into the workflow, teams can maintain faster-than-real-time performance, enabling monitoring, prediction, and operational decisions based on current conditions rather than historical snapshots.

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Design of Experiments: Explore the space, not one run at a time

Large optimization and uncertainty quantification campaigns can generate hundreds of simulation runs, making it impractical to store full-field results for every ensemble member. Extract quantities of interest directly during runtime and generate compact summaries at full temporal fidelity. Those summaries can train surrogate models and feed optimization drivers, enabling adaptive sampling workflows that automatically determine where to explore the design space next.

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