ARRID: ANN-based Rotordynamics for Robust and Integrated Design
- URL: http://arxiv.org/abs/2208.12640v1
- Date: Thu, 25 Aug 2022 16:08:05 GMT
- Title: ARRID: ANN-based Rotordynamics for Robust and Integrated Design
- Authors: Soheyl Massoudi, J\"urg Schiffmann
- Abstract summary: ARRID offers fast performance information, including the effect of manufacturing deviations.
The designer can manipulate the parameters of the design and the operating conditions to obtain performance information in a matter of seconds.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: The purpose of this study is to introduce ANN-based software for the fast
evaluation of rotordynamics in the context of robust and integrated design. It
is based on a surrogate model made of ensembles of artificial neural networks
running in a Bokeh web application. The use of a surrogate model has sped up
the computation by three orders of magnitude compared to the current models.
ARRID offers fast performance information, including the effect of
manufacturing deviations. As such, it helps the designer to make optimal design
choices early in the design process. The designer can manipulate the parameters
of the design and the operating conditions to obtain performance information in
a matter of seconds.
Related papers
- Cliqueformer: Model-Based Optimization with Structured Transformers [102.55764949282906]
We develop a model that learns the structure of an MBO task and empirically leads to improved designs.
We evaluate Cliqueformer on various tasks, ranging from high-dimensional black-box functions to real-world tasks of chemical and genetic design.
arXiv Detail & Related papers (2024-10-17T00:35:47Z) - Generative AI-based Prompt Evolution Engineering Design Optimization With Vision-Language Model [22.535058343006828]
We present a prompt evolution design optimization (PEDO) framework contextualized in a vehicle design scenario.
We use a physics-based solver and a vision-language model for practical or functional guidance in the generated car designs.
Our investigations on a car design optimization problem show a wide spread of potential car designs generated at the early phase of the search.
arXiv Detail & Related papers (2024-06-13T14:11:19Z) - Diffusion Model for Data-Driven Black-Box Optimization [54.25693582870226]
We focus on diffusion models, a powerful generative AI technology, and investigate their potential for black-box optimization.
We study two practical types of labels: 1) noisy measurements of a real-valued reward function and 2) human preference based on pairwise comparisons.
Our proposed method reformulates the design optimization problem into a conditional sampling problem, which allows us to leverage the power of diffusion models.
arXiv Detail & Related papers (2024-03-20T00:41:12Z) - Compositional Generative Inverse Design [69.22782875567547]
Inverse design, where we seek to design input variables in order to optimize an underlying objective function, is an important problem.
We show that by instead optimizing over the learned energy function captured by the diffusion model, we can avoid such adversarial examples.
In an N-body interaction task and a challenging 2D multi-airfoil design task, we demonstrate that by composing the learned diffusion model at test time, our method allows us to design initial states and boundary shapes.
arXiv Detail & Related papers (2024-01-24T01:33:39Z) - Latent Diffusion Models for Structural Component Design [11.342098118480802]
This paper proposes a framework for the generative design of structural components.
We employ a Latent Diffusion model to generate potential designs of a component that can satisfy a set of problem-specific loading conditions.
arXiv Detail & Related papers (2023-09-20T19:28:45Z) - 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) - Design of Unmanned Air Vehicles Using Transformer Surrogate Models [8.914156789222266]
We develop an AI Designer that synthesizes novel unmanned aerial vehicles (UAVs) designs.
Our approach uses a deep transformer model with a novel domain-specific encoding such that we can evaluate the performance of new proposed designs without running expensive flight dynamics models and CAD tools.
arXiv Detail & Related papers (2022-11-11T21:22:21Z) - Efficient Automatic Machine Learning via Design Graphs [72.85976749396745]
We propose FALCON, an efficient sample-based method to search for the optimal model design.
FALCON features 1) a task-agnostic module, which performs message passing on the design graph via a Graph Neural Network (GNN), and 2) a task-specific module, which conducts label propagation of the known model performance information.
We empirically show that FALCON can efficiently obtain the well-performing designs for each task using only 30 explored nodes.
arXiv Detail & Related papers (2022-10-21T21:25:59Z) - Investigating Positive and Negative Qualities of Human-in-the-Loop
Optimization for Designing Interaction Techniques [55.492211642128446]
Designers reportedly struggle with design optimization tasks where they are asked to find a combination of design parameters that maximizes a given set of objectives.
Model-based computational design algorithms assist designers by generating design examples during design.
Black box methods for assistance, on the other hand, can work with any design problem.
arXiv Detail & Related papers (2022-04-15T20:40:43Z) - Generative Design by Reinforcement Learning: Enhancing the Diversity of
Topology Optimization Designs [5.8010446129208155]
This study proposes a reinforcement learning based generative design process, with reward functions maximizing the diversity of topology designs.
We show that RL-based generative design produces a large number of diverse designs within a short inference time by exploiting GPU in a fully automated manner.
arXiv Detail & Related papers (2020-08-17T06:50:47Z)
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.