Publications related to Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

On the density of the supremum of the solution to the linear stochastic heat equation

We study the regularity of the probability density function of the supremum of the solution to the linear stochastic heat equation. Using a general criterion for the smoothness of densities for locally nondegenerate random variables, we establish the smoot ...

SPRINGER2020

Inverse Problems and Optimal Power Flow in Active Distribution Networks

Cong Wang

During the last decade, distribution networks have experienced essential changes driven by the integration of renewable-energy sources, batteries, electric-vehicle charging stations, etc. This results in not only opportunities, but also operational problem ...

EPFL2019

Sequential Outlier Detection Based on Incremental Decision Trees

Hakan Gökcesu, Kaan Gökcesu

We introduce an online outlier detection algorithm to detect outliers in a sequentially observed data stream. For this purpose, we use a two-stage filtering and hedging approach. In the first stage, we construct a multimodal probability density function to ...

2019

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Volkan Cevher, Paul Thierry Yves Rolland, Ali Kavis

We study the fundamental problem of learning an unknown, smooth probability function via pointwise Bernoulli tests. We provide a scalable algorithm for efficiently solving this problem with rigorous guarantees. In particular, we prove the convergence rate ...

2019

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Fei Pu

In this thesis, we study systems of linear and/or non-linear stochastic heat equations and fractional heat equations in spatial dimension

1

driven by space-time white noise. The main topic is the study of hitting probabilities for the solutions to these ...

EPFL2018

Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow

Colin Neil Jones, Yuejiang Liu, Georgios Stathopoulos, Jean-Hubert Hours

The optimal power flow (OPF) problem, a fundamental problem in power systems, is generally nonconvex and computationally challenging for networks with an increasing number of smart devices and real-time control requirements. In this paper, we first investi ...

2017

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

Alfio Quarteroni, Andrea Manzoni, Federico Negri

The numerical solution of partial differential equations (PDEs) depending on para- metrized or random input data is computationally intensive. Reduced order modeling techniques, such as the reduced basis methods, have been developed to alleviate this compu ...

MATHICSE2016

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Napat Rujeerapaiboon

Dynamic optimization problems affected by uncertainty are ubiquitous in many application domains. Decision makers typically model the uncertainty through random variables governed by a probability distribution. If the distribution is precisely known, then ...

EPFL2016

Probability density function of the electric field in diffuse correlation spectroscopy of human bone in vivo

Dimitri Nestor Alice Van De Ville

Diffuse correlation spectroscopy (DCS) is the technique of choice for non-invasive assessments of human bone blood flow. However, DCS classical algorithms are based on the fundamental assumption that the electric field of the light reaching the DCS photode ...

Optical Soc Amer2016

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

Daniel Kuhn, Grani Adiwena Hanasusanto

We present a risk-averse multi-dimensional newsvendor model for a class of products whose demands are strongly correlated and subject to fashion trends that are not fully understood at the time when orders are placed. The demand distribution is known to be ...

Springer Verlag2015

Solving Stochastic AC Power Flow via Polynomial Chaos Expansion

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On the density of the supremum of the solution to the linear stochastic heat equation

Inverse Problems and Optimal Power Flow in Active Distribution Networks

Sequential Outlier Detection Based on Incremental Decision Trees

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

Dimensionality Reduction in Dynamic Optimization under Uncertainty

Probability density function of the electric field in diffuse correlation spectroscopy of human bone in vivo

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

MATHICSE Technical Report : Dimensionality reduction of parameter-dependent problems through proper orthogonal decomposition

The stochastic heat equation: hitting probabilities and the probability density function of the supremum via Malliavin calculus

Distributionally Robust Multi-Item Newsvendor Problems with Multimodal Demand Distributions

Sequential Outlier Detection Based on Incremental Decision Trees

Probability density function of the electric field in diffuse correlation spectroscopy of human bone in vivo

Real-Time Distributed Algorithms for Nonconvex Optimal Power Flow

On the density of the supremum of the solution to the linear stochastic heat equation

Inverse Problems and Optimal Power Flow in Active Distribution Networks

Efficient learning of smooth probability functions from Bernoulli tests with guarantees.

Dimensionality Reduction in Dynamic Optimization under Uncertainty