Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design aircraft structures. We mainly analyze influence Mach number, overload, angle attack, elevator deflection, altitude, other factors on loads key monitoring components, based which input output variables ...

nonlinear knapsack problems (nkp) are the alternative formulation for the multiple-choice knapsack problems. a powerful approach for solving nkp is dynamic programming which may obtain the global op-timal solution even in the case of discrete solution space for these problems. despite the power of this solu-tion approach, it computationally performs very slowly when the solution space of the pr...

A special class of the knapsack problem is called the separable nonlinear knapsack problem. This problem has received considerable attention recently because of its numerous applications. Dynamic programming is one of the basic approaches for solving this problem. Unfortunately, the size of state-pace will dramatically increase and cause the dimensionality problem. In this paper, an efficient a...

In this work, we consider two kinds of model reduction techniques to simulate blood flow through the largest systemic arteries, where a stenosis is located in a peripheral artery i.e. in an artery that is located far away from the heart. For our simulations we place the stenosis in one of the tibial arteries belonging to the right lower leg (right post tibial artery). The model reduction techni...

We propose a deep learning-based surrogate model for stochastic simulators. The basic idea is to use generative neural network approximate the response. challenge with such framework resides in designing architecture and selecting loss-function suitable While we utilize simple feed-forward network, conditional maximum mean discrepancy (CMMD) as loss function. CMMD exploits property of reproduci...