3 citations · 4 across the 6 of their papers we have counts for
6 papers
Monte Carlo Event Generation with Continuous Normalizing Flows
Enrico Bothmann, Timo Janßen, Max Knobbe +2
We apply Continuous Normalizing Flows trained with the Flow Matching method to the problem of phase-space sampling in Monte Carlo event generation for high-energy collider physics.…
Density estimation from batched broken random samples
Hancheng Bi, Bernhard Schmitzer, Thilo D. Stier
The broken random sample problem was first introduced by DeGroot, Feder, and Gole (1971, Ann. Math. Statist.): in each observation (batch), a random sample of i.i.d. point pair…
Efficient many-jet event generation with Flow Matching
Enrico Bothmann, Timo Janßen, Max Knobbe +2
We apply for the first time the Flow Matching method to the problem of phase-space sampling for event generation in high-energy collider physics. By training the model to remap the…
Entropic transfer operators for stochastic systems
Hancheng Bi, Clément Sarrazin, Bernhard Schmitzer +1
Dynamical systems can be analyzed via their Frobenius-Perron transfer operator and its estimation from data is an active field of research. Recently entropic transfer operators hav…
Transfer Operators from Batches of Unpaired Points via Entropic Transport Kernels
Florian Beier, Hancheng Bi, Clément Sarrazin +2
In this paper, we are concerned with estimating the joint probability of random variables and , given independent observation blocks $(\boldsymbol{x}^i,\boldsymbol{y}^i)…
Manifold learning in Wasserstein space
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer +1
This paper aims at building the theoretical foundations for manifold learning algorithms in the space of absolutely continuous probability measures …