Integrating scientific knowledge with machine learning for engineering and environmental systems
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Deep generative models in engineering design: A review
L Regenwetter, AH Nobari… - Journal of …, 2022 - asmedigitalcollection.asme.org
Automated design synthesis has the potential to revolutionize the modern engineering
design process and improve access to highly optimized and customized products across …
design process and improve access to highly optimized and customized products across …
[PDF][PDF] Integrating physics-based modeling with machine learning: A survey
There is a growing consensus that solutions to complex science and engineering problems
require novel methodologies that are able to integrate traditional physics-based modeling …
require novel methodologies that are able to integrate traditional physics-based modeling …
Deep generative modeling for mechanistic-based learning and design of metamaterial systems
Metamaterials are emerging as a new paradigmatic material system to render
unprecedented and tailorable properties for a wide variety of engineering applications …
unprecedented and tailorable properties for a wide variety of engineering applications …
A review of the application of machine learning and data mining approaches in continuum materials mechanics
Machine learning tools represent key enablers for empowering material scientists and
engineers to accelerate the development of novel materials, processes and techniques. One …
engineers to accelerate the development of novel materials, processes and techniques. One …
Deep generative design: Integration of topology optimization and generative models
S Oh, Y Jung, S Kim, I Lee… - Journal of …, 2019 - asmedigitalcollection.asme.org
Deep learning has recently been applied to various research areas of design optimization.
This study presents the need and effectiveness of adopting deep learning for generative …
This study presents the need and effectiveness of adopting deep learning for generative …
Computational design and manufacturing of sustainable materials through first-principles and materiomics
Engineered materials are ubiquitous throughout society and are critical to the development
of modern technology, yet many current material systems are inexorably tied to widespread …
of modern technology, yet many current material systems are inexorably tied to widespread …
Deep learning approaches for mining structure-property linkages in high contrast composites from simulation datasets
Data-driven methods are emerging as an important toolset in the studies of multiscale,
multiphysics, materials phenomena. More specifically, data mining and machine learning …
multiphysics, materials phenomena. More specifically, data mining and machine learning …
A review of machine learning methods applied to structural dynamics and vibroacoustic
Abstract The use of Machine Learning (ML) has rapidly spread across several fields of
applied sciences, having encountered many applications in Structural Dynamics and …
applied sciences, having encountered many applications in Structural Dynamics and …
Guiding the design of heterogeneous electrode microstructures for Li‐ion batteries: microscopic imaging, predictive modeling, and machine learning
Electrochemical and mechanical properties of lithium‐ion battery materials are heavily
dependent on their 3D microstructure characteristics. A quantitative understanding of the …
dependent on their 3D microstructure characteristics. A quantitative understanding of the …