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Smart Workflow Recovery and Resource Management
Keep multi-step simulation workflows moving with more control over parallel execution and failure recovery. Rescale Workflows now supports concurrent node limits, node-level failure controls, and failed-run resubmission, helping teams running design of experiments (DOEs) and other simulation campaigns manage how much work runs at once, contain failures at the step where they occur, and recover with greater precision.
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Advanced Modeling & Simulation Workflow Templates
Get started with reusable simulation pipelines using pre-built Workflow Templates from Rescale. Browse curated templates for parallel processing, dynamic hardware routing, centralized job configuration, and agent-assisted validation, then run them with your inputs or duplicate and adapt them for your team, turning common workflow patterns into working pipelines in minutes.
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How to Run Design of Experiments on Rescale Using Rescale Assistant and Workflows
Engineers running parameter studies on Rescale now have two new approaches for setting up and executing Design of Experiments, both powered by the Rescale Assistant and Workflows. Depending on the study’s complexity, engineers can choose between Workflow Runs for maximum flexibility or a single Workflow Template for centralized control.
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Seizing the Agentic Advantage: The Use Cases Rewiring Digital Engineering
The engineering of physical products is entering its biggest transformation in thirty years.
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Streamline AI Physics Model Training and Deployment with Auto Tune
Simplify surrogate model development with AI Physics Auto Tune, coming soon to Rescale. Set a search duration and let AutoTune explore hyperparameter configurations in parallel, remove weak candidates early, fully train the strongest options, and select the best model across four production-ready architectures, without requiring deep AI/ML expertise.
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HiLiftAeroML Open Dataset Accelerates AI Physics Model Adoption
Develop aerodynamic AI Physics models with HiLiftAeroML, an open-source dataset now available natively within Rescale AI Physics. Built from surface-based simulation data across four geometries and multiple angles of attack, it gives engineers a practical starting point for training AI surrogate models without first assembling and building their own dataset.
