Search Results for author: Johannes Brandstetter

Found 14 papers, 7 papers with code

Lie Point Symmetry Data Augmentation for Neural PDE Solvers

no code implementations15 Feb 2022 Johannes Brandstetter, Max Welling, Daniel E. Worrall

In this paper, we present a method, which can partially alleviate this problem, by improving neural PDE solver sample complexity -- Lie point symmetry data augmentation (LPSDA).

Data Augmentation

Geometric and Physical Quantities Improve E(3) Equivariant Message Passing

1 code implementation ICLR 2022 Johannes Brandstetter, Rob Hesselink, Elise van der Pol, Erik J Bekkers, Max Welling

Including covariant information, such as position, force, velocity or spin is important in many tasks in computational physics and chemistry.

Boundary Graph Neural Networks for 3D Simulations

no code implementations21 Jun 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

The abundance of data has given machine learning considerable momentum in natural sciences and engineering.

Learning 3D Granular Flow Simulations

no code implementations4 May 2021 Andreas Mayr, Sebastian Lehner, Arno Mayrhofer, Christoph Kloss, Sepp Hochreiter, Johannes Brandstetter

Recently, the application of machine learning models has gained momentum in natural sciences and engineering, which is a natural fit due to the abundance of data in these fields.

Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER

no code implementations2 Dec 2020 Markus Holzleitner, Lukas Gruber, José Arjona-Medina, Johannes Brandstetter, Sepp Hochreiter

We prove under commonly used assumptions the convergence of actor-critic reinforcement learning algorithms, which simultaneously learn a policy function, the actor, and a value function, the critic.

reinforcement-learning

Quantum Optical Experiments Modeled by Long Short-Term Memory

no code implementations30 Oct 2019 Thomas Adler, Manuel Erhard, Mario Krenn, Johannes Brandstetter, Johannes Kofler, Sepp Hochreiter

In this work, we show that machine learning models can provide significant improvement over random search.

A GAN based solver of black-box inverse problems

no code implementations NeurIPS Workshop Deep_Invers 2019 Michael Gillhofer, Hubert Ramsauer, Johannes Brandstetter, Bernhard Schäfl, Sepp Hochreiter

We propose a GAN based approach to solve inverse problems which have non-differential or non-continuous forward relations.

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