Related Experiment Video
Updated: Jun 5, 2025

Stochastic Noise Application for the Assessment of Medial Vestibular Nucleus Neuron Sensitivity In Vitro
Published on: August 28, 2019
Remarks on Regularization by Noise, Convex Integration and Spontaneous Stochasticity
Franco Flandoli1, Marco Rehmeier1,2
1Faculty of Sciences, Scuola Normale Superiore, Pisa, Italy.
This study explores connections and distinctions between regularization by noise, convex integration, and spontaneous stochasticity. These methods examine how small perturbations in fluid dynamics affect large-scale behaviors, aiming to clarify their relationships and differences.
Area of Science:
- Fluid dynamics
- Stochastic processes
- Mathematical physics
Background:
- Small-scale perturbations in fluid dynamic equations can significantly impact large-scale behaviors.
- Existing research has explored regularization by noise, convex integration, and spontaneous stochasticity independently.
- The precise relationships and differences between these phenomena are not fully understood.
Purpose of the Study:
- To discuss the potential links and differences between regularization by noise, convex integration, and spontaneous stochasticity.
- To provide a comparative examination of these three topics.
- To stimulate new research into the effects of small-scale perturbations on fluid dynamics.
Main Methods:
- Comparative analysis of theoretical frameworks.
- Examination of the impact of small-scale perturbations on fluid dynamic equations.
- Literature review and conceptual synthesis.
Main Results:
- Identified commonalities between convex integration and spontaneous stochasticity.
- Highlighted contrasting effects, such as in regularization by noise.
- Acknowledged the lack of rigorous links and precise explanations for the observed phenomena.
Conclusions:
- The study provides a foundational comparison of three distinct but related concepts in fluid dynamics.
- Further research is needed to rigorously define the links and differences between these perturbation effects.
- This comparative examination serves as a catalyst for future investigations in the field.
Related Concept Videos
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Propagation of Uncertainty from Random Error
Random Error
Entropy Change in Reversible Processes
The statement can be further generalized to prove that entropy is a state function. Take a cyclic process between any two points on a p-V diagram.
Convolution Properties II
The width property indicates that if the durations of input signals are T1 and T2, then the width of the output response equals the sum of both durations, irrespective of the shapes of the two functions. For instance, convolving two rectangular pulses with durations of 2 seconds and 1 second results in a function with a width of 3 seconds.
The area property asserts that the area under the...
Random Variables
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...

