Tag: Neural Network

ScientificML

This paper investigates using artificial neural networks as surrogate models for black-box optimization, enabling efficient optimization when analytical function descriptions are unavailable by obtaining objective values through numerical simulation.

Quantum/Chemical

For quantum many-body physics and ab initio quantum chemistry, this work derives an unbiased direct gradient estimator and introduces the adaptive minimum-variance phase (AMVP) estimator to overcome training failures in weak-gradient regimes for neural-network variational optimization.

ScientificML

Proposes a novel framework using set-based value function characterization and neural network approximation to estimate stabilization domains and stabilizing controllers for input-constrained discrete-time nonlinear systems.