A survey of machine learning techniques in structural and multidisciplinary optimization
Abstract Machine Learning (ML) techniques have been used in an extensive range of
applications in the field of structural and multidisciplinary optimization over the last few …
applications in the field of structural and multidisciplinary optimization over the last few …
Deep learning for multifidelity aerodynamic distribution modeling from experimental and simulation data
Wind-tunnel experiment plays a critical role in the design and development phases of
modern aircraft, which is always limited by prohibitive cost. In contrast, numerical simulation …
modern aircraft, which is always limited by prohibitive cost. In contrast, numerical simulation …
Nonlinear reduced order modeling using domain decomposition
View Video Presentation: https://doi. org/10.2514/6.2022-1250. vid As designers become
increasingly reliant upon expensive, high-fidelity numerical modeling and simulation …
increasingly reliant upon expensive, high-fidelity numerical modeling and simulation …
Manifold alignment-based multi-fidelity reduced-order modeling applied to structural analysis
This work presents the application of a recently developed parametric, non-intrusive, and
multi-fidelity reduced-order modeling method on high-dimensional displacement and stress …
multi-fidelity reduced-order modeling method on high-dimensional displacement and stress …
Manifold alignment-based nonintrusive and nonlinear multifidelity reduced-order modeling
This study presents the development of a methodology for the construction of data-driven,
parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with …
parametric, multifidelity reduced-order models to emulate aerodynamic flowfields with …
A physics-based domain adaptation framework for modeling and forecasting building energy systems
State-of-the-art machine-learning-based models are a popular choice for modeling and
forecasting energy behavior in buildings because given enough data, they are good at …
forecasting energy behavior in buildings because given enough data, they are good at …
Multifidelity Methodology for Reduced-Order Models with High-Dimensional Inputs
In the early stages of aerospace design, reduced-order models (ROMs) are crucial for
minimizing computational costs associated with using physics-rich field information in many …
minimizing computational costs associated with using physics-rich field information in many …
A Reduced Order Modeling Approach to Blunt-Body Aerodynamic Modeling
HV Dean, K Decker, BE Robertson… - AIAA SCITECH 2024 …, 2024 - arc.aiaa.org
Blunt-body entry vehicles display complex flow phenomena that results in dynamic
instabilities in the low supersonic to transonic flight regime. Dynamic stability coefficients are …
instabilities in the low supersonic to transonic flight regime. Dynamic stability coefficients are …
Application of Transfer Learning for Multi-Fidelity Regression using Physics-Informed Neural Network on an Airfoil
View Video Presentation: https://doi. org/10.2514/6.2022-0386. vid Recent developments in
highly expressive neural networks and automatic differentiation have enabled the …
highly expressive neural networks and automatic differentiation have enabled the …
Design Optimization Based on Multi-fidelity Metamodels
A Clarich, L Battaglia, C Poloni, L Parussini… - … Methods and Design for …, 2024 - Springer
This paper illustrates how multi-fidelity metamodels can be efficiently applied to save time
and costs in parametric design optimization, which normally requires simulating numerically …
and costs in parametric design optimization, which normally requires simulating numerically …