Undergrad Distribution function for specific 1D problem

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The discussion revolves around a 1D problem where a particle starts at X=0 and disappears if it moves to X<0, influenced by a known velocity probability density function F[v]. When the average velocity is negative, it is concluded that particles will eventually disappear, raising the question of the distribution function of these particles along the X-axis. To model this, initial positions, dependencies between velocities over time, and the continuous nature of velocity are essential, suggesting a Wiener Process with drift as a potential model. The simplest scenario involves independent particles with a constant drift and volatility, focusing on the average trajectory of multiple particles to determine their probability distribution on the X-axis. Ultimately, the inquiry seeks to establish the probability density function for the particles at any fixed time, particularly near the boundary at X=0.
gugk
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Hello!
Maybe someone will be able to suggest something about the following quite simple problem:

1D problem on axis "X". Particle moves only along "X" axis and starts its motion from X=0. However, when "X<0" particle disappears. Particle is influenced by some kind of force in such way that we know only the density probability function of particle velocity F[v]. In the case, when the average velocity (can be found from F[v]) is negative, we can conclude that any particle after some time eventually will disappear, since the probability to go to negative "X" is bigger.

The question: if we have infinite number of such particles (and as a result infinite number of trajectories that ends on X<0), what is distribution function for these particles on the axis "X"?

Thank you very much for any suggestions and relevant to this problem sources.
 
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More information would be needed to model this, including:
1. what are the initial positions of all the particles? This could be given as a distribution, for example a uniform distribution on [0,1].
2. what, if any, are the dependencies between
(a) velocities of particle p at time s and at time t (##s\neq t##)
(b) velocities of particle p and particle q at time t (##p\neq q##)
(c) velocities of particle p at time s and particle q at time t (##p\neq q## and ##s\neq t##)

For the problem to make any physical sense the velocity of a particle would need to be a continuous function of time, which requires strong dependency in (a). A Wiener Process (Brownian Motion with drift) could be a suitable process for the velocity of a particle. That requires specification of two parameters for each particle ##p##:
I. instantaneous drift ##\mu_p## of velocity
II. instantaneous volatility ##\sigma_p## of velocity

A simple model would have ##\mu_p=\mu\forall p## and ##\sigma_p=\sigma\forall p## and all particles' velocity processes independent of one another.
 
Sorry that I didn't formulate the problem clear enough from the beginning.
I am interested in the simplest case, when particles are independent and velocity is a continuous function of time - (a) we know only the probability to have some velocity at the next time moment (from the probability density function of particle velocity F[v]).

To imagine the problem in different way, we can think that we do experiment:
we put the first particle at X=0 and record it trajectory. When the first particle eventually disappears (since the probability to move to negative X bigger), we put the second particle and do the same procedure. Each particle will have its own trajectory. If we average all these trajectories, we obtain the most probable trajectory - that says what is the probability to find the particle on some interval.

Or similarly, we can think that on the X<0 there is a wall that does not allow particle to move to negative value of X (particle doesn't bounce from the wall, just stay at X=0 if the force is directed to negative direction of X). And, assuming that from the beginning of that motion passed long time (initial condition will stop play the role, the particle mostly will be near the wall, even if it started far away), what is the probability density function for this particle on the axis X? What is the probability to find particle at some interval on the axis X in any fixed time?
 
Here is a little puzzle from the book 100 Geometric Games by Pierre Berloquin. The side of a small square is one meter long and the side of a larger square one and a half meters long. One vertex of the large square is at the center of the small square. The side of the large square cuts two sides of the small square into one- third parts and two-thirds parts. What is the area where the squares overlap?

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