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Exhaust Aftertreatment Modeling Optimize SCR, DPF, and other aftertreatment system designs through high-fidelity multiphysics simulation to meet stringent emission standards. Uncertainty Analysis Quantify uncertainties in input parameters and models to enhance the reliability of simulation predictions and the robustness of product design. Surrogate Modeling Build lightweight surrogate models of high-fidelity simulations for rapid parameter exploration, optimization, and uncertainty quantification. Model Order Reduction Reduce high-dimensional complex models via mathematical projection while preserving core dynamics, enabling real-time simulation and embedded deployment. Spray & Combustion Simulation Accurately simulate fuel spray atomization, evaporation, mixing, and combustion processes to provide key insights for engine performance and emission optimization. Realtime Simulation Generate high-fidelity real-time dynamic models for Hardware-in-the-Loop testing, control system development, and Driver-in-the-Loop simulation.
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Nodes: The Modular Intelligence Platform for CAE Specialists

Nodes is a modular intelligence platform designed for CAE engineers, seamlessly integrating advanced data-driven algorithms with traditional physics-based simulation. It empowers engineers to build complex surrogate models, perform large-scale model reduction, and execute parametric optimizations— all in a no-code environment. Bridging the gap between AI and engineering simulation, Nodes makes advanced analytics accessible, efficient, and highly customizable.

Intuitive Interface: A No-Code, Node-RED Inspired Environment

The user interface of Nodes is built around a visual, drag-and-drop workflow designer, inspired by Node-RED. This design philosophy ensures that engineers can focus on solving problems without writing a single line of code.

  • Visual Workflow Canvas: A central workspace where users can drag, drop, and connect modular components to define their analytics pipeline.
  • Intuitive Navigation: Clean and logically organized layout, with clear separation between the workflow area, algorithm library, and configuration panels.
  • Real-Time Feedback: The interface provides immediate visual cues for connection validity, execution progress, and error detection.
  • Customizable Workspace: Users can save, export, and share workflows, promoting collaboration and reproducibility across teams.

Algorithms & Plugins: A Comprehensive Sidebar Library

Nodes features an extensible sidebar housing a rich collection of pre-built algorithms and plugins, curated to address the core needs of CAE specialists.

  • Wide-Ranging Algorithm Sets: Includes modules for:
    • Machine Learning: Regression, classification, clustering, and neural networks.
    • Surrogate Modeling: Gaussian Processes, Polynomial Chaos Expansion, Radial Basis Functions, and more.
    • Model Order Reduction: Proper Orthogonal Decomposition (POD), Reduced Basis Methods, and hyper-reduction techniques.
    • Data Mining: Feature extraction, dimensionality reduction, outlier detection, and pattern recognition.
  • Plugin Ecosystem: Supports custom plugins, allowing organizations to integrate proprietary or third-party algorithms.
  • Seamless Integration: All algorithms are designed to work cohesively with CAE data formats (e.g., CSV, HDF5, finite element results) and CAE solvers (e.g., ANSYS, Abaqus, OpenFOAM, and more)

Algorithm Configuration Window: Fine-Tuned Control

Double-clicking any algorithm icon in the workflow opens a detailed configuration window, providing granular control over parameters and settings.

  • Context-Aware Parameters: The configuration window displays only relevant parameters based on the selected algorithm, reducing complexity.
  • Interactive Input Validation: Real-time validation of inputs with tooltips, recommended ranges, and descriptive documentation for each parameter.
  • Preset Management: Users can save, load, and share parameter presets, ensuring consistency and best practices across projects.
  • Live Previews: For certain algorithms (e.g., dimensionality reduction or clustering), a preview of results updates dynamically as parameters are adjusted.