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Found 7 papers, 1 papers with code
Stochastic differential equations for performance analysis of wireless communication systems
This paper addresses the difficulty of characterizing the time-varying nature of fading channels.
Residual Multi-Fidelity Neural Network Computing
In this work, we consider the general problem of constructing a neural network surrogate model using multi-fidelity information.
Physics-informed Spectral Learning: the Discrete Helmholtz--Hodge Decomposition
In this work, we further develop the Physics-informed Spectral Learning (PiSL) by Espath et al. \cite{Esp21} based on a discrete $L^2$ projection to solve the discrete Hodge--Helmholtz decomposition from sparse data.
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
We train the resulting principal component flow (PCF) on data of PV and wind power generation as well as load demand in Germany in the years 2013 to 2015.
Multilevel Monte Carlo with Numerical Smoothing for Robust and Efficient Computation of Probabilities and Densities
This study is motivated by the computation of probabilities of events, pricing options with a discontinuous payoff, and density estimation problems for dynamics where the discretization of the underlying stochastic processes is necessary.
Importance sampling for a robust and efficient multilevel Monte Carlo estimator for stochastic reaction networks
The multilevel Monte Carlo (MLMC) method for continuous time Markov chains, first introduced by Anderson and Higham (2012), is a highly efficient simulation technique that can be used to estimate various statistical quantities for stochastic reaction networks (SRNs), and in particular for stochastic biological systems.
Numerical Analysis Numerical Analysis Computation 60H35, 60J27, 60J75, 92C40
Mean-Field Learning: a Survey
Most of learning algorithms for games with continuous action spaces are limited to strict contraction best reply maps in which the Banach-Picard iteration converges with geometrical convergence rate.
