Pages that link to "Item:Q555926"
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The following pages link to Approximating a finite element model by neural network prediction for facility optimization in groundwater engineering (Q555926):
Displaying 11 items.
- Intermittency and multiscale dynamics in milling of fiber reinforced composites (Q399811) (← links)
- Neural network algorithm based on Legendre improved extreme learning machine for solving elliptic partial differential equations (Q780292) (← links)
- Effects of spindle speed-dependent dynamic characteristics of ball bearing and multi-modes on the stability of milling processes (Q904867) (← links)
- Estimation of heterogeneous aquifer parameters from piezometric data using ridge functions and neural networks (Q1423630) (← links)
- TRANSFORM-ANN for online optimization of complex industrial processes: casting process as case study (Q1694351) (← links)
- A novel improved extreme learning machine algorithm in solving ordinary differential equations by Legendre neural network methods (Q1716363) (← links)
- Modeling the Neuman's well function by an artificial neural network for the determination of unconfined aquifer parameters (Q1787661) (← links)
- Online surrogate multiobjective optimization algorithm for contaminated groundwater remediation designs (Q1988875) (← links)
- Surrogate optimization of deep neural networks for groundwater predictions (Q2046338) (← links)
- Geochemical equilibrium determination using an artificial neural network in compositional reservoir flow simulation (Q2185985) (← links)
- Runge-Kutta methods for a semi-analytical prediction of milling stability (Q2517539) (← links)
