Generative Parametric Design (GPD): A framework for real-time geometry generation and on-the-fly multiparametric approximation.
Revolutionizing Engineering Design with Real-time AI
This paper presents a novel paradigm in simulation-based engineering sciences by introducing a new framework called Generative Parametric Design (GPD). The GPD framework enables the generation of new designs along with their corresponding parametric solutions given as a reduced basis. To achieve this, two Rank Reduction Autoencoders (RRAEs) are employed, one for encoding and generating the design or geometry, and the other for encoding the sparse Proper Generalized Decomposition (sPGD) mode solutions. These models are linked in the latent space using regression techniques, allowing efficient transitions between design and their associated sPGD modes. By empowering design exploration and optimization, this framework also advances digital and hybrid twin development, enhancing predictive modeling and real-time decision-making in engineering applications.
Key Metrics & Enterprise Impact
The Generative Parametric Design (GPD) framework fundamentally transforms engineering workflows, offering tangible improvements across predictive modeling, design cycles, and computational efficiency.
Enhanced Predictive Modeling
GPD's ability to provide on-the-fly multiparametric approximations significantly enhances predictive modeling, making it invaluable for digital and hybrid twin development.
0 Prediction Accuracy Improvement 0 Model Deployment SpeedFaster Design Exploration & Optimization
By generating new designs and solutions in real-time, GPD empowers engineers to explore a vast design space more efficiently, leading to optimized outcomes.
0 Design Cycle Reduction 0 Optimization IterationsReduced Computational Overhead
The framework's use of reduced-basis solutions and efficient latent space representations drastically cuts down computational costs for complex simulations.
0 Simulation Compute Cost Savings 0 Hardware Resource ReductionDeep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
GPD: A Novel Engineering Paradigm
The Generative Parametric Design (GPD) framework is introduced as a novel paradigm in simulation-based engineering sciences. It enables the generation of new designs and their corresponding parametric solutions, given as a reduced basis, which is critical for digital and hybrid twin development.
Core Methodology: RRAEs & sPGD Integration
GPD employs two Rank Reduction Autoencoders (RRAEs): one for encoding and generating the design/geometry, and the other for encoding sparse Proper Generalized Decomposition (sPGD) mode solutions. These RRAEs are linked in the latent space using regression techniques, allowing efficient transitions and predictions.
Addressing Key Engineering Challenges
GPD addresses the critical need to balance simulation accuracy, computational efficiency, and generative design capabilities, particularly in data-scarce and high-dimensional problem spaces, overcoming limitations of traditional methods.
Microstructure Application Case Study
The framework's effectiveness is demonstrated on two-phase microstructures, generating novel configurations and constructing associated multi-parametric solutions for microscale fields governed by material parameters.
GPD Framework
Introduces Generative Parametric Design for real-time geometry & multiparametric approximation.
Enterprise Process Flow
Geometry Encoding & Generation via RRAE1
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sPGD Mode Solution Encoding via RRAE2
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Latent Space Linking with Regression
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Real-time Design Exploration & Prediction
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Case Study: Microstructure Application Case Study
Challenge: Design and analyze two-phase microstructures with varying inclusion shapes, sizes, and orientations, and predict their multiparametric stress responses in real-time.
Solution: GPD generated novel microstructure designs (images) and predicted corresponding sPGD mode solutions (spatial and parametric functions) from material parameters. Two RRAEs were trained for geometry and sPGD modes, linked by an MLP.
Results: Demonstrated successful generation of diverse microstructures and accurate real-time prediction of their multiparametric responses in a reduced-order form. Highlights GPD's potential for systematic design exploration and optimization in materials science.
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Your AI Implementation Roadmap
A strategic outline for integrating Generative Parametric Design into your enterprise, ensuring a smooth transition and maximum impact.
Phase 1: Data Curation & RRAE Foundation
Identify and gather relevant high-fidelity design and simulation datasets. Train the initial RRAEs models for both geometry encoding and sPGD mode representation to establish robust latent spaces.
Phase 2: Latent Space Integration & Model Training
Develop and train regression models (MLPs) to link the geometry's latent space representation to the sPGD mode solutions' latent spaces. Validate the predictive accuracy of this integrated framework.
Phase 3: Real-time Deployment & Workflow Integration
Integrate the GPD framework into existing engineering design and simulation workflows. Implement real-time generation and prediction capabilities for on-the-fly design exploration and decision-making.
Phase 4: Advanced Features & Scalability
Explore adaptive RRAE models for automatic hyperparameter tuning. Extend the framework to handle 3D geometries, incorporate nonlinear/anisotropic material responses, and support multiphysics couplings for broader industrial applicability.
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