Publications related to Stochastic differential equation

Functional data analysis with rough sample paths?

Victor Panaretos, Neda Mohammadi Jouzdani

Functional data are typically modeled as sample paths of smooth stochastic processes in order to mitigate the fact that they are often observed discretely and noisily, occasionally irregularly and sparsely. The smoothness assumption is imposed to allow for ...

TAYLOR & FRANCIS LTD2023

Stochastic Modeling of Bedload Transport and Bar Development in Shallow Flow: Linear Stability Analysis and Numerical Simulation

Mehrdad Kiani Oshtorjani

Field surveys and laboratory experiments show that bedload transport rates may vary to within one order of magnitude for a given water discharge. One of today's major challenges is to account for these large transport rate fluctuations in computational hyd ...

2023

Static and Dynamic Stochastic Analysis of a Temperature-Sensitive VO2 Spiking Neuron

Mihai Adrian Ionescu, Teodor Rosca

In this work, we investigate the intrinsic cycle-to-cycle variations in a spiking temperature-sensitive neuron, based on the resistive switching of a Vanadium dioxide (VO2) two-terminal device. We study how this phenomenon impacts the spike rate jitter, an ...

New York2023

Multiplicative chaos of the Brownian loop soup

Antoine Pierre François Jego, Titus Lupu

We construct a measure on the thick points of a Brownian loop soup in a bounded domain D

D

of the plane with given intensity theta>0

\theta >0

, which is formally obtained by exponentiating the square root of its occupation field. The measure is construct ...

WILEY2023

Detecting whether a stochastic process is finitely expressed in a basis

Victor Panaretos, Neda Mohammadi Jouzdani

Is it possible to detect if the sample paths of a stochastic process almost surely admit a finite expansion with respect to some/any basis? The determination is to be made on the basis of a finite collection of discretely/noisily observed sample paths. We ...

ACADEMIC PRESS INC ELSEVIER SCIENCE2023

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Giancarlo Ferrari Trecate, Luca Furieri, Clara Lucía Galimberti, Liang Xu

Deep Neural Networks (DNNs) training can be difficult due to vanishing and exploding gradients during weight optimization through backpropagation. To address this problem, we propose a general class of Hamiltonian DNNs (H-DNNs) that stem from the discretiz ...

2023

Lp-regularity theory for the stochastic reaction-diffusion equation with super-linear multiplicative noise and strong dissipativity

Jaeyun Yi

We study the existence, uniqueness, and regularity of the solution to the stochastic reaction-diffusion equation (SRDE) with colored noise F-center dot:partial derivative(t)u = aijuxixj +biuxi + cu - bu1+beta +xi u1+gamma F-center dot, (t, x) is ...

San Diego2023

Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds

Aleksandra Radenovic, Georg Fantner, Vytautas Navikas, Barney Frederick Drake, Helena Miljkovic, Simon Finn Mayer, Sanjin Marion

In current nanopore-based label-free single-molecule sensing technologies, stochastic processes influence the selection of translocating molecule, translocation rate and translocation velocity. As a result, single-molecule translocations are challenging to ...

2023

Geometrically-Conditioned Point Diffusion Models

Pascal Fua, Eduard Trulls Fortuny, Michal Jan Tyszkiewicz

Diffusion models generating images conditionally on text, such as Dall-E 2 [51] and Stable Diffusion[53], have recently made a splash far beyond the computer vision com- munity. Here, we tackle the related problem of generating point clouds, both unconditi ...

2023

Real-time implementation of the high-fidelity NBI code RABBIT into the discharge control system of ASDEX Upgrade

Federico Alberto Alfredo Felici, Bernhard Sieglin

For the first time, a real-time capable NBI code, which has a comparable fidelity to the much more computationally expensive Monte Carlo codes such as NUBEAM, has been coupled to the discharge control system of a tokamak. This implementation has been done ...

IOP Publishing Ltd2023

Stochastic differential equation

Graph Chatbot

Chat with Graph Search

Functional data analysis with rough sample paths?

Stochastic Modeling of Bedload Transport and Bar Development in Shallow Flow: Linear Stability Analysis and Numerical Simulation

Static and Dynamic Stochastic Analysis of a Temperature-Sensitive VO2 Spiking Neuron

Multiplicative chaos of the Brownian loop soup

Detecting whether a stochastic process is finitely expressed in a basis

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Lp-regularity theory for the stochastic reaction-diffusion equation with super-linear multiplicative noise and strong dissipativity

Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds

Geometrically-Conditioned Point Diffusion Models

Real-time implementation of the high-fidelity NBI code RABBIT into the discharge control system of ASDEX Upgrade

Static and Dynamic Stochastic Analysis of a Temperature-Sensitive VO2 Spiking Neuron

Stochastic Modeling of Bedload Transport and Bar Development in Shallow Flow: Linear Stability Analysis and Numerical Simulation

Hamiltonian Deep Neural Networks Guaranteeing Non-Vanishing Gradients by Design

Lp-regularity theory for the stochastic reaction-diffusion equation with super-linear multiplicative noise and strong dissipativity

Multiplicative chaos of the Brownian loop soup

Functional data analysis with rough sample paths?

Detecting whether a stochastic process is finitely expressed in a basis

Spatially multiplexed single-molecule translocations through a nanopore at controlled speeds

Real-time implementation of the high-fidelity NBI code RABBIT into the discharge control system of ASDEX Upgrade

Geometrically-Conditioned Point Diffusion Models