Hyper-parameter optimization: A review of algorithms and applications
T Yu, H Zhu - arXiv preprint arXiv:2003.05689, 2020 - arxiv.org
Since deep neural networks were developed, they have made huge contributions to
everyday lives. Machine learning provides more rational advice than humans are capable of …
everyday lives. Machine learning provides more rational advice than humans are capable of …
Advances in computational intelligence of polymer composite materials: machine learning assisted modeling, analysis and design
The superior multi-functional properties of polymer composites have made them an ideal
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
choice for aerospace, automobile, marine, civil, and many other technologically demanding …
Digital twin: Values, challenges and enablers from a modeling perspective
Digital twin can be defined as a virtual representation of a physical asset enabled through
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
data and simulators for real-time prediction, optimization, monitoring, controlling, and …
Physics-informed multi-LSTM networks for metamodeling of nonlinear structures
This paper introduces an innovative physics-informed deep learning framework for
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
metamodeling of nonlinear structural systems with scarce data. The basic concept is to …
Review of hybrid-electric aircraft technologies and designs: Critical analysis and novel solutions
Reducing greenhouse gas emissions has become a priority for civil transport aviation. One
of the possible solutions investigated by current aeronautics research is the introduction of …
of the possible solutions investigated by current aeronautics research is the introduction of …
Managing computational complexity using surrogate models: a critical review
In simulation-based realization of complex systems, we are forced to address the issue of
computational complexity. One critical issue that must be addressed is the approximation of …
computational complexity. One critical issue that must be addressed is the approximation of …
Physics-guided convolutional neural network (PhyCNN) for data-driven seismic response modeling
Accurate prediction of building's response subjected to earthquakes makes possible to
evaluate building performance. To this end, we leverage the recent advances in deep …
evaluate building performance. To this end, we leverage the recent advances in deep …
Recent progress in minimizing the warpage and shrinkage deformations by the optimization of process parameters in plastic injection molding: A review
The quality control of plastic products is an essential aspect of the plastic injection molding
(PIM) process. However, the warpage and shrinkage deformations continue to exist because …
(PIM) process. However, the warpage and shrinkage deformations continue to exist because …
The future of risk assessment
E Zio - Reliability Engineering & System Safety, 2018 - Elsevier
Risk assessment must evolve for addressing the existing and future challenges, and
considering the new systems and innovations that have already arrived in our lives and that …
considering the new systems and innovations that have already arrived in our lives and that …
A framework for data-driven analysis of materials under uncertainty: Countering the curse of dimensionality
A new data-driven computational framework is developed to assist in the design and
modeling of new material systems and structures. The proposed framework integrates three …
modeling of new material systems and structures. The proposed framework integrates three …