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Erkundung der neuesten Anwendungsfälle im Bereich Digital Engineering, Branchentrends und Innovationen der Rescale-Plattform.
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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…
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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.
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Agentic Multi-Disciplinary Design Exploration with Ansys CFX and Mechanical
Connect CFD insight to downstream engineering decisions with an agentic Ansys CFX and Mechanical workflow on Rescale. The workflow combines the power of agentic engineering with deterministic simulation across multiple disciplines, analyzing a completed CFX design study, identifying key parameters and data gaps, kicking off follow-on studies, and then brings CFX pressure loads into Mechanical…
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Coreweave Multi-node Workload Support
Rescale has now extended its job submission environment to support multi-node MPI and Kubernetes-based workloads running on CoreWeave Cloud, bringing enterprise-grade HPC capabilities to a cloud infrastructure option that offers meaningfully lower cost for large-scale simulation. The capability has been validated end-to-end across multi-node configurations, with stability confirmed through scale testing and initial production use…
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Simulation Guide Agent: Expert Guidance for Every Engineer, Tailored to Any Workflow
Make simulation expertise easier to access across your organization. Rescale’s Simulation Guide Agent uses trusted technical documentation, user guides, templates, and team best practices to help users ask better questions, interpret results, select an appropriate workflow, configure jobs correctly, and verify recommendations before taking action, all within the existing simulation workflow on Rescale.
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Agent Foundations: The Building Blocks of Agentic Engineering Success
Rescale’s purpose-built platform agents embed agentic actions into existing workflows, creating a trusted path towards end-to-end agentic engineering processes.
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Predictive Maintenance Digital Twins: Connect Simulation, Python, and Machine Learning
Build digital twin workflows that connect simulation and machine learning in one engineering process. Rescale helps teams move beyond isolated analyses by linking physics-based simulation, custom code, and data-driven models so digital twin initiatives can become more practical, repeatable, and useful in day-to-day engineering work.
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Multi-stage Simulation Workflows for Computational Chemistry
Run multi-stage computational chemistry workflows without manual handoffs between steps. Rescale helps research teams connect simulation stages, compute environments, and downstream analysis so complex studies move forward in a more repeatable, scalable workflow instead of depending on ad hoc scripts and one-off operational effort.
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Automated CAD-to-CFD Design Loops with Data Lake Integration
Shorten the loop between design changes and aerodynamic insight. Rescale can connect updated CAD inputs, CFD workflows, and engineering data pipelines so new designs automatically trigger analysis, feed results into a shared data foundation, and return faster feedback to the teams shaping the product.
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Parametric Optimization via Local AI Inference
Explore more design options faster with local AI Physics inference. Rescale’s inference tooling lets engineers connect geometry, surrogate models, and outputs in a lightweight design workflow so they can run parametric studies and optimization loops directly on their own device (e.g. local workstation) without needing inference servers or HPC clusters.
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File Sharing Across Multi-Step Simulation Workflows
Move files across multi-step workflows without manual hand-offs or separate storage setup. Rescale Workflows can now use cloud storage to pass files directly between job and workstation steps, helping engineering teams keep preprocessing, solve, and post-processing stages connected inside one reusable workflow.
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Compute Economics: How Engineering Teams Spend Smarter on Rescale
Rescale’s novel cost controls help engineering and IT leaders reduce computing spending while preserving the benefits of cloud–all without changing the way engineering teams work.
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Agent-Accelerated Computational Chemistry
Accelerate computational chemistry workflows with agentic support for molecular dynamics simulation on Rescale. Agents help researchers analyze results, propose next steps, configure new simulations, and critique outcomes with scientific context preserved, connecting HPC execution, scientific analysis, historical knowledge, and job setup into one guided workflow that keeps the researcher in control.
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AI Physics for Automotive Crash and Structural FEA Simulations
Build surrogate models for crash and structural simulation with Rescale’s integrated AI Physics workflow for transient FEA data. The updated crash use case brings GeoTransolver support for transient behavior, solver-specific metadata extraction, and training-ready formatting into one reusable pipeline—helping engineers move toward faster crash insight with less manual setup.
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Evaluation Framework for Engineering Agents
Deploy engineering agents with confidence with Rescale’s agent evaluation approach, anchored by the Rescale Agent Fidelity Toolkit (RAFT). Rescale RAFT combines response scoring, tool-use validation, ground-truth checks, and continuous monitoring to systematically verify that agents deliver relevant answers and take correct actions, providing the quality discipline that production agentic engineering demands.
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Rescale Interlink: Open-Source Data Transfer and Job Management
Move simulation data between your desktop and the cloud faster with Rescale Interlink, an open-source hybrid CLI and GUI tool for managing files and jobs on Rescale. FIPS 140-3 compliant and available on Windows, Mac, and Linux, Interlink delivers multithreaded parallel transfers, a visual file browser, auto-download of completed job results, and full job submission,…