research
∙
07/11/2023
Multi-index Importance Sampling for McKean-Vlasov Stochastic Differential Equation
This work introduces a novel approach that combines the multi-index Mont...
share
research
∙
06/30/2023
Scalable method for Bayesian experimental design without integrating over posterior distribution
We address the computational efficiency in solving the A-optimal Bayesia...
research
∙
06/05/2023
Automated Importance Sampling via Optimal Control for Stochastic Reaction Networks: A Markovian Projection-based Approach
We propose a novel alternative approach to our previous work (Ben Hammou...
research
∙
03/09/2023
Modeling metallic fatigue data using the Birnbaum–Saunders distribution
This work employs the Birnbaum–Saunders distribution to model the fatigu...
research
∙
02/27/2023
Double-loop quasi-Monte Carlo estimator for nested integration
Nested integration arises when a nonlinear function is applied to an int...
research
∙
02/25/2023
Data-driven uncertainty quantification for constrained stochastic differential equations and application to solar photovoltaic power forecast data
In this work, we extend the data-driven Itô stochastic differential equa...
research
∙
02/21/2023
Physics-informed Spectral Learning: the Discrete Helmholtz–Hodge Decomposition
In this work, we further develop the Physics-informed Spectral Learning ...
research
∙
02/13/2023
Uncertainty quantification in coastal aquifers using the multilevel Monte Carlo method
We consider a class of density-driven flow problems. We are particularly...
research
∙
10/25/2022
Deep nurbs – admissible neural networks
In this study, we propose a new numerical scheme for physics-informed ne...
research
∙
08/05/2022
Multilevel Importance Sampling for McKean-Vlasov Stochastic Differential Equation
This work combines multilevel Monte Carlo methods with importance sampli...
research
∙
07/14/2022
Single Level Importance Sampling for McKean-Vlasov Stochastic Differential Equation
This paper investigates Monte Carlo methods to estimate probabilities of...
research
∙
03/15/2022
Optimal Damping with Hierarchical Adaptive Quadrature for Efficient Fourier Pricing of Multi-Asset Options in Lévy Models
Efficient pricing of multi-asset options is a challenging problem in qua...
research
∙
03/08/2022
Nonlinear Isometric Manifold Learning for Injective Normalizing Flows
To model manifold data using normalizing flows, we propose to employ the...
research
∙
01/04/2022
Efficient Importance Sampling Algorithm Applied to the Performance Analysis of Wireless Communication Systems Estimation
When assessing the performance of wireless communication systems operati...
research
∙
12/13/2021
Small-noise approximation for Bayesian optimal experimental design with nuisance uncertainty
Calculating the expected information gain in optimal Bayesian experiment...
research
∙
11/02/2021
Numerical Smoothing with Hierarchical Adaptive Sparse Grids and Quasi-Monte Carlo Methods for Efficient Option Pricing
When approximating the expectation of a functional of a stochastic proce...
research
∙
10/27/2021
Efficient Importance Sampling via Stochastic Optimal Control for Stochastic Reaction Networks
We explore the efficient estimation of statistical quantities, particula...
research
∙
09/22/2021
On the equivalence of different adaptive batch size selection strategies for stochastic gradient descent methods
In this study, we demonstrate that the norm test and inner product/ortho...
research
∙
06/15/2021
Machine learning-based conditional mean filter: a generalization of the ensemble Kalman filter for nonlinear data assimilation
Filtering is a data assimilation technique that performs the sequential ...
research
∙
05/05/2021
Analysis of a class of Multi-Level Markov Chain Monte Carlo algorithms based on Independent Metropolis-Hastings
In this work, we present, analyze, and implement a class of Multi-Level ...
research
∙
04/21/2021
Principal Component Density Estimation for Scenario Generation Using Normalizing Flows
Neural networks-based learning of the distribution of non-dispatchable r...
research
∙
04/15/2021
Multi-index ensemble Kalman filtering
In this work we marry multi-index Monte Carlo with ensemble Kalman filte...
research
∙
02/04/2021
Wind Field Reconstruction with Adaptive Random Fourier Features
We investigate the use of spatial interpolation methods for reconstructi...
research
∙
01/23/2021
Efficient Importance Sampling for Large Sums of Independent and Identically Distributed Random Variables
We aim to estimate the probability that the sum of nonnegative independe...
research
∙
01/04/2021
A simple approach to proving the existence, uniqueness, and strong and weak convergence rates for a broad class of McKean–Vlasov equations
By employing a system of interacting stochastic particles as an approxim...
research
∙
10/05/2020
Smaller generalization error derived for deep compared to shallow residual neural networks
Estimates of the generalization error are proved for a residual neural n...
research
∙
09/02/2020
Weak error rates for option pricing under the rough Bergomi model
In quantitative finance, modeling the volatility structure of underlying...
research
∙
08/04/2020
A note on tools for prediction under uncertainty and identifiability of SIR-like dynamical systems for epidemiology
We provide an overview of the methods that can be used for prediction un...
research
∙
06/29/2020
A Derivative Tracking Model for Wind Power Forecast Error
Reliable wind power generation forecasting is crucial for applications s...
research
∙
04/08/2020
A Wasserstein Coupled Particle Filter for Multilevel Estimation
In this paper, we consider the filtering problem for partially observed ...
research
∙
03/06/2020
Generalized Parallel Tempering on Bayesian Inverse Problems
In the current work we present two generalizations of the Parallel Tempe...
research
∙
02/02/2020
Multilevel Ensemble Kalman Filtering with local-level Kalman gains
We introduce a new multilevel ensemble Kalman filtering method (MLEnKF) ...
research
∙
11/14/2019
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 chai...
research
∙
08/28/2019
A Universal Splitting Estimator for the Performance Evaluation of Wireless Communications Systems
We propose a unified rare-event estimator for the performance evaluation...
research
∙
05/31/2019
Solution of the 3D density-driven groundwater flow problem with uncertain porosity and permeability
As groundwater is an essential nutrition and irrigation resource, its po...
research
∙
05/06/2019
Propagation of Uncertainties in Density-Driven Flow
Accurate modeling of contamination in subsurface flow and water aquifers...
research
∙
03/13/2019
An Accurate Sample Rejection Estimator for the Estimation of Outage Probability of EGC Receivers
In this work, we evaluate the outage probability (OP) for L-branch equal...
research
∙
01/26/2019
Eficient Monte Carlo Simulation of the Left Tail of Positive Gaussian Quadratic Forms
Estimating the left tail of quadratic forms in Gaussian random vectors i...
research
∙
10/03/2018
Multilevel Monte Carlo Acceleration of Seismic Wave Propagation under Uncertainty
We interpret uncertainty in the parameters of a model for seismic wave p...
research
∙
05/09/2018
Spatial Poisson Processes for Fatigue Crack Initiation
In this work we propose a stochastic model for estimating the occurrence...
research
∙
11/26/2017
Ensemble-marginalized Kalman filter for linear time-dependent PDEs with noisy boundary conditions: Application to heat transfer in building walls
In this work, we present the ensemble-marginalized Kalman filter (EnMKF)...
research
∙
11/12/2017
On the Sum of Order Statistics and Applications to Wireless Communication Systems Performances
We consider the problem of evaluating the cumulative distribution functi...
research
∙
10/17/2012