38 citations · 48 across the 8 of their papers we have counts for
8 papers
Concentration of Stationary Measures onto Large Scales in the Fast-Advection Limit of the Stochastically Forced Two-Dimensional Navier-Stokes Equations
Tobias Rohner
We investigate the fast-advection limit () of the stochastically forced two-dimensional incompressible Navier-Stokes equations on the torus. Numerical simulation…
Computing statistical Euler limits of the Navier--Stokes equations in three dimensions
Johannes L. Grafen, Tobias Rohner, Stephan Simonis
We develop a Monte Carlo lattice Boltzmann method to compute statistical solutions to the three-dimensional incompressible Navier--Stokes and Euler equations. Entropic space-time a…
Sufficient Conditions for the Energy Balance for the Stochastic Incompressible Euler Equations with Additive Noise in two Space Dimensions
Tobias Rohner, Franziska Weber
We consider vanishing viscosity approximations to solutions of the stochastic incompressible Euler equations in two space dimensions with additive noise. We identify sufficient and…
Generative AI for fast and accurate statistical computation of fluids
Roberto Molinaro, Samuel Lanthaler, Bogdan Raonić +9
We present a generative AI algorithm for addressing the pressing task of fast, accurate, and robust statistical computation of three-dimensional turbulent fluid flows. Our algorith…
Poseidon: Efficient Foundation Models for PDEs
Maximilian Herde, Bogdan Raonić, Tobias Rohner +4
We introduce Poseidon, a foundation model for learning the solution operators of PDEs. It is based on a multiscale operator transformer, with time-conditioned layer norms that enab…
Efficient Computation of Large-Scale Statistical Solutions to Incompressible Fluid Flows
Tobias Rohner, Siddhartha Mishra
This work presents the development, performance analysis and subsequent optimization of a GPU-based spectral hyperviscosity solver for turbulent flows described by the three dimens…
Approximate Reversible Circuits for NISQ-Era Quantum Computers
Niels Gleinig, Tobias Rohner, Torsten Hoefler
The synthesis approaches for quantum circuits typically aim at minimizing the number of lines or gates. Given the tight restrictions on those logical resources in physical implemen…
Convolutional Neural Operators for robust and accurate learning of PDEs
Bogdan Raonić, Roberto Molinaro, Tim De Ryck +5
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been large…