SimJEB: Simulated Jet Engine Bracket Dataset
- URL: http://arxiv.org/abs/2105.03534v1
- Date: Fri, 7 May 2021 23:24:21 GMT
- Title: SimJEB: Simulated Jet Engine Bracket Dataset
- Authors: Eamon Whalen, Azariah Beyene, Caitlin Mueller
- Abstract summary: This paper introduces the Simulated Jet Engine Bracket dataset (SimJEB)
SimJEB is a new, public collection of crowdsourced mechanical brackets and high-fidelity structural simulations.
The models in SimJEB were collected from the original submissions to the GrabCAD Jet Engine Bracket Challenge.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Recent advancements in geometric deep learning have enabled a new class of
engineering surrogate models; however, few existing shape datasets are
well-suited to evaluate them. This paper introduces the Simulated Jet Engine
Bracket Dataset (SimJEB): a new, public collection of crowdsourced mechanical
brackets and high-fidelity structural simulations designed specifically for
surrogate modeling. SimJEB models are more complex, diverse, and realistic than
the synthetically generated datasets commonly used in parametric surrogate
model evaluation. In contrast to existing engineering shape collections,
SimJEB's models are all designed for the same engineering function and thus
have consistent structural loads and support conditions. The models in SimJEB
were collected from the original submissions to the GrabCAD Jet Engine Bracket
Challenge: an open engineering design competition with over 700 hand-designed
CAD entries from 320 designers representing 56 countries. Each model has been
cleaned, categorized, meshed, and simulated with finite element analysis
according to the original competition specifications. The result is a
collection of diverse, high-quality and application-focused designs for
advancing geometric deep learning and engineering surrogate models.
Related papers
- Attention to Detail: Fine-Scale Feature Preservation-Oriented Geometric Pre-training for AI-Driven Surrogate Modeling [6.34618828355523]
AI-driven surrogate modeling has become an increasingly effective alternative to physics-based simulations for 3D design, analysis, and manufacturing.
This work introduces a self-supervised geometric representation learning method designed to capture fine-scale geometric features from non-parametric 3D models.
arXiv Detail & Related papers (2025-04-27T17:10:13Z) - DeepWheel: Generating a 3D Synthetic Wheel Dataset for Design and Performance Evaluation [3.3148826359547523]
This study proposes a synthetic design-performance dataset generation framework using generative AI.
The framework first generates 2D rendered images using Stable Diffusion, and then reconstructs the 3D geometry through 2.5D depth estimation.
The final dataset, named DeepWheel, consists of over 6,000 photo-realistic images and 900 structurally analyzed 3D models.
arXiv Detail & Related papers (2025-04-15T16:20:00Z) - AGENT: An Aerial Vehicle Generation and Design Tool Using Large Language Models [13.674483311866183]
AGENT learns powerful representations of aircraft designs directly from textual files.
AGENT can issue evaluations of designs allowing it to act as a surrogate model of the physics simulation that underlies the AircraftVerse dataset.
arXiv Detail & Related papers (2025-04-11T21:13:10Z) - BARE: Combining Base and Instruction-Tuned Language Models for Better Synthetic Data Generation [71.46236155101032]
We propose Base-Refine, a synthetic data generation method that combines the diversity of base models with the quality of instruct-tuned models.
We show that fine-tuning with BARE-generated data achieves a 101% improvement over instruct-only data on GSM8K and a 18.4% improvement over SOTA methods on RAFT.
arXiv Detail & Related papers (2025-02-03T00:12:40Z) - STAR: Synthesis of Tailored Architectures [61.080157488857516]
We propose a new approach for the synthesis of tailored architectures (STAR)
Our approach combines a novel search space based on the theory of linear input-varying systems, supporting a hierarchical numerical encoding into architecture genomes. STAR genomes are automatically refined and recombined with gradient-free, evolutionary algorithms to optimize for multiple model quality and efficiency metrics.
Using STAR, we optimize large populations of new architectures, leveraging diverse computational units and interconnection patterns, improving over highly-optimized Transformers and striped hybrid models on the frontier of quality, parameter size, and inference cache for autoregressive language modeling.
arXiv Detail & Related papers (2024-11-26T18:42:42Z) - Exploring the design space of deep-learning-based weather forecasting systems [56.129148006412855]
This paper systematically analyzes the impact of different design choices on deep-learning-based weather forecasting systems.
We study fixed-grid architectures such as UNet, fully convolutional architectures, and transformer-based models.
We propose a hybrid system that combines the strong performance of fixed-grid models with the flexibility of grid-invariant architectures.
arXiv Detail & Related papers (2024-10-09T22:25:50Z) - VehicleSDF: A 3D generative model for constrained engineering design via surrogate modeling [3.746111274696241]
This work explores the use of 3D generative models to explore the design space in the context of vehicle development.
We generate diverse 3D models of cars that meet a given set of geometric specifications.
We also obtain quick estimates of performance parameters such as aerodynamic drag.
arXiv Detail & Related papers (2024-10-09T16:59:24Z) - Generative Aerodynamic Design with Diffusion Probabilistic Models [0.7373617024876725]
We show that generative models have the potential to provide geometries by generalizing geometries over a large dataset of simulations.
In particular, we leverage diffusion probabilistic models trained on XFOIL simulations to synthesize two-dimensional airfoil geometries conditioned on given aerodynamic features and constraints.
We show that the models are able to generate diverse candidate designs for identical requirements and constraints, effectively exploring the design space to provide multiple starting points to optimization procedures.
arXiv Detail & Related papers (2024-09-20T08:38:36Z) - Computer Vision Model Compression Techniques for Embedded Systems: A Survey [75.38606213726906]
This paper covers the main model compression techniques applied for computer vision tasks.
We present the characteristics of compression subareas, compare different approaches, and discuss how to choose the best technique.
We also share codes to assist researchers and new practitioners in overcoming initial implementation challenges.
arXiv Detail & Related papers (2024-08-15T16:41:55Z) - DrivAerNet++: A Large-Scale Multimodal Car Dataset with Computational Fluid Dynamics Simulations and Deep Learning Benchmarks [25.00264553520033]
DrivAerNet++ comprises 8,000 diverse car designs modeled with high-fidelity computational fluid dynamics (CFD) simulations.
The dataset includes diverse car configurations such as fastback, notchback, and estateback, with different underbody and wheel designs to represent both internal combustion engines and electric vehicles.
This dataset supports a wide array of machine learning applications including data-driven design optimization, generative modeling, surrogate model training, CFD simulation acceleration, and geometric classification.
arXiv Detail & Related papers (2024-06-13T23:19:48Z) - Mechanistic Design and Scaling of Hybrid Architectures [114.3129802943915]
We identify and test new hybrid architectures constructed from a variety of computational primitives.
We experimentally validate the resulting architectures via an extensive compute-optimal and a new state-optimal scaling law analysis.
We find MAD synthetics to correlate with compute-optimal perplexity, enabling accurate evaluation of new architectures.
arXiv Detail & Related papers (2024-03-26T16:33:12Z) - AircraftVerse: A Large-Scale Multimodal Dataset of Aerial Vehicle
Designs [15.169540193173923]
AircraftVerse contains 27,714 diverse air vehicle designs.
Each design comprises the following artifacts: a symbolic design tree describing topology propulsion subsystem, battery subsystem, and design details.
We present baseline surrogate models that use different modalities of design representation to predict design performance metrics.
arXiv Detail & Related papers (2023-06-08T21:07:15Z) - Beyond Statistical Similarity: Rethinking Metrics for Deep Generative
Models in Engineering Design [10.531935694354448]
This paper doubles as a review and practical guide to evaluation metrics for deep generative models (DGMs) in engineering design.
We first summarize the well-accepted classic' evaluation metrics for deep generative models grounded in machine learning theory.
Next, we curate a set of design-specific metrics which can be used for evaluating deep generative models.
arXiv Detail & Related papers (2023-02-06T16:34:16Z) - Design Space Exploration and Explanation via Conditional Variational
Autoencoders in Meta-model-based Conceptual Design of Pedestrian Bridges [52.77024349608834]
This paper provides a performance-driven design exploration framework to augment the human designer through a Conditional Variational Autoencoder (CVAE)
The CVAE is trained on 18'000 synthetically generated instances of a pedestrian bridge in Switzerland.
arXiv Detail & Related papers (2022-11-29T17:28:31Z) - Model LEGO: Creating Models Like Disassembling and Assembling Building Blocks [53.09649785009528]
In this paper, we explore a paradigm that does not require training to obtain new models.
Similar to the birth of CNN inspired by receptive fields in the biological visual system, we propose Model Disassembling and Assembling.
For model assembling, we present the alignment padding strategy and parameter scaling strategy to construct a new model tailored for a specific task.
arXiv Detail & Related papers (2022-03-25T05:27:28Z) - Hybrid modeling: Applications in real-time diagnosis [64.5040763067757]
We outline a novel hybrid modeling approach that combines machine learning inspired models and physics-based models.
We are using such models for real-time diagnosis applications.
arXiv Detail & Related papers (2020-03-04T00:44:57Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.