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08/10/2023
PDE-Refiner: Achieving Accurate Long Rollouts with Neural PDE Solvers
Time-dependent partial differential equations (PDEs) are ubiquitous in s...
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05/24/2023
Learning Lagrangian Fluid Mechanics with E(3)-Equivariant Graph Neural Networks
We contribute to the vastly growing field of machine learning for engine...
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05/18/2023
Clifford Group Equivariant Neural Networks
We introduce Clifford Group Equivariant Neural Networks: a novel approac...
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03/31/2023
E(3) Equivariant Graph Neural Networks for Particle-Based Fluid Mechanics
We contribute to the vastly growing field of machine learning for engine...
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02/17/2023
G-Signatures: Global Graph Propagation With Randomized Signatures
Graph neural networks (GNNs) have evolved into one of the most popular d...
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02/13/2023
Geometric Clifford Algebra Networks
We propose Geometric Clifford Algebra Networks (GCANs) that are based on...
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01/24/2023
ClimaX: A foundation model for weather and climate
Most state-of-the-art approaches for weather and climate modeling are ba...
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09/30/2022
Towards Multi-spatiotemporal-scale Generalized PDE Modeling
Partial differential equations (PDEs) are central to describing complex ...
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09/08/2022
Clifford Neural Layers for PDE Modeling
Partial differential equations (PDEs) see widespread use in sciences and...
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06/07/2022
Few-Shot Learning by Dimensionality Reduction in Gradient Space
We introduce SubGD, a novel few-shot learning method which is based on t...
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02/15/2022
Lie Point Symmetry Data Augmentation for Neural PDE Solvers
Neural networks are increasingly being used to solve partial differentia...
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02/07/2022
Message Passing Neural PDE Solvers
The numerical solution of partial differential equations (PDEs) is diffi...
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10/06/2021
Geometric and Physical Quantities improve E(3) Equivariant Message Passing
Including covariant information, such as position, force, velocity or sp...
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06/21/2021
Boundary Graph Neural Networks for 3D Simulations
The abundance of data has given machine learning huge momentum in natura...
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05/04/2021
Learning 3D Granular Flow Simulations
Recently, the application of machine learning models has gained momentum...
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12/02/2020
Convergence Proof for Actor-Critic Methods Applied to PPO and RUDDER
We prove under commonly used assumptions the convergence of actor-critic...
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10/13/2020
Cross-Domain Few-Shot Learning by Representation Fusion
In order to quickly adapt to new data, few-shot learning aims at learnin...
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09/29/2020
Align-RUDDER: Learning From Few Demonstrations by Reward Redistribution
Reinforcement Learning algorithms require a large number of samples to s...
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07/16/2020
Hopfield Networks is All You Need
We show that the transformer attention mechanism is the update rule of a...
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11/10/2019
Using LSTMs for climate change assessment studies on droughts and floods
Climate change affects occurrences of floods and droughts worldwide. How...
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10/30/2019