Design of morlet wavelet neural network for solving a class of singular pantograph nonlinear differential models
The aim of this study is to design a layer structure of feed-forward artificial neural networks
using the Morlet wavelet activation function for solving a class of pantograph differential …
using the Morlet wavelet activation function for solving a class of pantograph differential …
Parabolic PDE-constrained optimal control under uncertainty with entropic risk measure using quasi-Monte Carlo integration
We study the application of a tailored quasi-Monte Carlo (QMC) method to a class of optimal
control problems subject to parabolic partial differential equation (PDE) constraints under …
control problems subject to parabolic partial differential equation (PDE) constraints under …
Morlet wavelet neural network investigations to present the numerical investigations of the prediction differential model
In this study, a design of Morlet wavelet neural networks (MWNNs) is presented to solve the
prediction differential model (PDM) by applying the global approximation capability of a …
prediction differential model (PDM) by applying the global approximation capability of a …
Risk-adaptive approaches to learning and decision making: A survey
JO Royset - arXiv preprint arXiv:2212.00856, 2022 - arxiv.org
Uncertainty is prevalent in engineering design, statistical learning, and decision making
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …
broadly. Due to inherent risk-averseness and ambiguity about assumptions, it is common to …
An exact penalty function optimization method and its application in stress constrained topology optimization and scenario based reliability design problems
H Liao, X Yuan, R Gao - Applied Mathematical Modelling, 2024 - Elsevier
A smooth penalty function method which does not involve dual and slack implicit variables to
formulate constrained optimization conditions is proposed. New active and loss functions …
formulate constrained optimization conditions is proposed. New active and loss functions …
A binary tournament competition algorithm for solving partial differential equation constrained optimization via finite element method
Sometimes, in a tournament competition to form a single team from two teams, 50% players
are selected from the union of both teams according to their performance (fitness) …
are selected from the union of both teams according to their performance (fitness) …
Taylor approximation for chance constrained optimization problems governed by partial differential equations with high-dimensional random parameters
We propose a fast and scalable optimization method to solve chance or probabilistic
constrained optimization problems governed by partial differential equations (PDEs) with …
constrained optimization problems governed by partial differential equations (PDEs) with …
First-order Pontryagin maximum principle for risk-averse stochastic optimal control problems
In this paper, we derive first-order Pontryagin optimality conditions for risk-averse stochastic
optimal control problems subject to final time inequality constraints whose costs are general …
optimal control problems subject to final time inequality constraints whose costs are general …
Multifidelity conditional value-at-risk estimation by dimensionally decomposed generalized polynomial chaos-Kriging
We propose novel methods for Conditional Value-at-Risk (CVaR) estimation for nonlinear
systems under high-dimensional dependent random inputs. We develop a novel DD-GPCE …
systems under high-dimensional dependent random inputs. We develop a novel DD-GPCE …
Risk-adapted optimal experimental design
Constructing accurate statistical models of critical system responses typically requires an
enormous amount of experimental data. Unfortunately, physical experimentation is often …
enormous amount of experimental data. Unfortunately, physical experimentation is often …