Abstract: In this paper, by introducing nonlinear adjoint functions, the domination-monotonicity conditions that ensure the well-posedness of forward-backward stochastic differential equations (FBSDEs ...
Herein hybrid methods use approximate deterministic calculations to create biasing parameters for Monte Carlo. For source-detector problems, the CADIS[1] method uses the result of an adjoint ...
Creative Commons (CC): This is a Creative Commons license. Attribution (BY): Credit must be given to the creator. The development of low-loss reconfigurable integrated optical devices enables further ...
/path/to/miniconda3/envs/mp/lib/python3.11/site-packages/gplugins/gmeep/meep_adjoint_optimization.py:148: UserWarning: Settingrectangle_S5_5_LWG_CFal_d89a89d8_2500 ...
This work presents the mathematical/theoretical framework of the “nth-Order Feature Adjoint Sensitivity Analysis Methodology for Response-Coupled Forward/Adjoint ...
Simulating nuclear well-logging devices with Monte Carlo methods is computationally challenging and requires significant variance reduction to compute detector responses with low statistical ...
Abstract: This paper proposes a pole-residue-based adjoint neuro-transfer function (neuro-TF) technique with electromagnetic (EM) sensitivity analysis for parametric modeling of EM behavior of ...
Starting with the binomial coefficient and using its infinite product representation, the infinite product representation of the gamma function and of the zeta function are composed of an exponential ...
On the contrary to the direct approach, the adjoint approach starts from the Lagrangian formulation of the unconstrained optimization problem: where F is the objective function, S is the set of state ...
Some results have been hidden because they may be inaccessible to you
Show inaccessible results
Feedback