1 citations · 1 across the 8 of their papers we have counts for
9 papers
Stochastic Lifting for Generating Trajectories of Stochastic Physical Systems
Jules Berman, Tobias Blickhan, Benjamin Peherstorfer
Many stochastic physical systems evolve smoothly over time in the sense that the distribution of states changes regularly across time steps. The transition from current state to th…
Two-Parameter Flows for Learning Population Dynamics of Physical Systems
Paul Schwerdtner, Tobias Blickhan, Benjamin Peherstorfer
This work addresses the problem of learning the dynamics of high-dimensional probability densities over time using unlabeled samples, without assuming access to trajectory informat…
Leveraging Gauge Freedom for Learning Non-Gradient Population Dynamics of Stochastic Systems
Jules Berman, Tobias Blickhan, Benjamin Peherstorfer
Existing work on population dynamics inference often focuses on flows arising from vector fields that are the gradients of scalar potentials. Among all admissible flows that are co…
Computational boundary specification in 3D fixed-boundary magnetohydrodynamic equilibrium modeling
Alan Kaptanoglu, Tobias Blickhan
Outside the core of the plasma, the plasma current and pressure rapidly transition to zero in a scrape-off or edge region or plasma-vacuum interface. However, existing tools for fi…
MRX: A differentiable 3D MHD equilibrium solver without nested flux surfaces
Tobias Blickhan, Julianne Stratton, Alan A. Kaptanoglu
This article introduces the 3D magnetohydrodynamic (MHD) equilibrium solver MRX, based on relaxation of a magnetic field to a lower energy equilibrium state via admissible variatio…
DICE: Discrete inverse continuity equation for learning population dynamics
Tobias Blickhan, Jules Berman, Andrew Stuart +1
We introduce the Discrete Inverse Continuity Equation (DICE) method, a generative modeling approach that learns the evolution of a stochastic process from given sample populations…
Parametric model reduction of mean-field and stochastic systems via higher-order action matching
Jules Berman, Tobias Blickhan, Benjamin Peherstorfer
The aim of this work is to learn models of population dynamics of physical systems that feature stochastic and mean-field effects and that depend on physics parameters. The learned…
A registration method for reduced basis problems using linear optimal transport
Tobias Blickhan
We present a registration method for model reduction of parametric partial differential equations with dominating advection effects and moving features. Registration refers to the…
Waserstein model reduction approach for parametrized flow problems in porous media
Beatrice Battisti, Tobias Blickhan, Guillaume Enchéry +3
The aim of this work is to build a reduced-order model for parametrized porous media equations. The main challenge of this type of problems is that the Kolmogorov width of the solu…