
Generation of smoothlyvarying infill configurations from a continuous menu of cell patterns and the asymptotic analysis of its mechanical behaviour
We here introduce a novel scheme for generating smoothlyvarying infill ...
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On speeding up an asymptoticanalysisbased homogenisation scheme for designing gradient porous structured materials using a zoning strategy
Gradient porous structured materials possess significant potential of be...
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Comparison of pharmacist evaluation of medication orders with predictions of a machine learning model
The objective of this work was to assess the clinical performance of an ...
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Machine Learning in Compiler Optimisation
In the last decade, machine learning based compilation has moved from an...
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Design Optimisation of PowerEfficient Submarine Line through Machine Learning
An optimised subsea system design for energyefficient SDM operation is ...
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Piecewise quadratic approximations of arbitrary error functions for fast and robust machine learning
Most of machine learning approaches have stemmed from the application of...
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Assessment of machine learning methods for statetostate approaches
It is well known that numerical simulations of highspeed reacting flows...
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Stiffness optimisation of graded microstructal configurations using asymptotic analysis and machine learning
The article is aimed to address a combinative use of asymptotic analysis and machine learning, for fast stiffness design of configurations infilled with smoothlyvarying graded microstructures. The discussion is conducted in the context of an improved asymptotichomogenisation topology optimisation (AHTO plus) framework (Zhu et al., 2019). It is demonstrated that machine learning can be employed to represent the key but implicit interrelationships between formulations obtained at different orders from asymptotic analysis. Moreover, in the context of microstructural homogenisation, asymptotic analysis helps offer a platform for machine learning to release its full potentials in function representation. Firstly, asymptotic analysis identifies a computational routine for data acquisition, thus the training data are sufficient in theory. Secondly, the number of input arguments for machine learning can be minimised based on the explicit results by asymptotic analysis, and the scale of the machine learning model in use is kept small. Thirdly, the input arguments for machine learning are shown to be complete. Then the situation where certain factors affecting the function relationship represented by machine learning is avoided. Other issues on incorporating machine learning into the AHTO plus framework, such as ensuring the positive definiteness of the homogenised elasticity tensor and the speedingup of the associated sensitivity analysis, are also discussed here. Numerical examples show that the use of machine learning in the AHTO plus scheme can bring about an acceleration by two orders of magnitude, if compared with the existing treatments of using a zoning strategy.
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