首页|Physics-data coupling-driven method to predict the penetration depth into concrete targets
Physics-data coupling-driven method to predict the penetration depth into concrete targets
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NETL
NSTL
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The projectile penetration process into concrete target is a nonlinear complex problem.With the increase of experiment data,the data-driven paradigm has exhibited a new feasible method to solve such complex prob-lem.However,due to poor quality of experimental data,the traditional machine learning(ML)methods,which are driven only by experimental data,have poor generalization capabilities and limited prediction accuracy.Therefore,this study intends to exhibit a ML method fusing the prior knowledge with experiment data.The new ML method can constrain the fitting to experimental data,improve the generalization ability and the predic-tion accuracy.Experimental results show that integrating domain prior knowledge can effectively improve the performance of the prediction model for penetration depth into concrete targets.
Penetration into concreteArtificial neural networksPrior knowledge fusionPrediction model
NETL
NSTL
万方数据
