15 citations · 51 across the 10 of their papers we have counts for
13 papers
Minimal Convolutional RNNs Accelerate Spatiotemporal Learning
Coşku Can Horuz, Sebastian Otte, Martin V. Butz +1
We introduce MinConvLSTM and MinConvGRU, two novel spatiotemporal models that combine the spatial inductive biases of convolutional recurrent networks with the training efficiency…
A Deep Learning Earth System Model for Efficient Simulation of the Observed Climate
Nathaniel Cresswell-Clay, Bowen Liu, Dale Durran +4
A key challenge for computationally intensive state-of-the-art Earth System models is to distinguish global warming signals from interannual variability. Here we introduce DLESyM,…
Inferring Underwater Topography with FINN
Coşku Can Horuz, Matthias Karlbauer, Timothy Praditia +3
Spatiotemporal partial differential equations (PDEs) find extensive application across various scientific and engineering fields. While numerous models have emerged from both physi…
Comparing and Contrasting DLWP Backbones on Navier-Stokes and Atmospheric Dynamics
Matthias Karlbauer, Danielle C. Maddix, Abdul Fatir Ansari +5
A large number of Deep Learning Weather Prediction (DLWP) architectures -- based on various backbones, including U-Net, Transformer, Graph Neural Network, and Fourier Neural Operat…
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
Matthias Karlbauer, Nathaniel Cresswell-Clay, Dale R. Durran +5
We present a parsimonious deep learning weather prediction model to forecast seven atmospheric variables with 3-h time resolution for up to one-year lead times on a 110-km global m…
Inductive biases in deep learning models for weather prediction
Jannik Thuemmel, Matthias Karlbauer, Sebastian Otte +8
Deep learning has gained immense popularity in the Earth sciences as it enables us to formulate purely data-driven models of complex Earth system processes. Deep learning-based wea…
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
Manuel Traub, Sebastian Otte, Tobias Menge +3
Our brain can almost effortlessly decompose visual data streams into background and salient objects. Moreover, it can anticipate object motion and interactions, which are crucial a…
Composing Partial Differential Equations with Physics-Aware Neural Networks
Matthias Karlbauer, Timothy Praditia, Sebastian Otte +3
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes. FINN implements a new way of combining the…
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
Timothy Praditia, Matthias Karlbauer, Sebastian Otte +3
Data-driven modeling of spatiotemporal physical processes with general deep learning methods is a highly challenging task. It is further exacerbated by the limited availability of…
Active Tuning
Sebastian Otte, Matthias Karlbauer, Martin V. Butz
We introduce Active Tuning, a novel paradigm for optimizing the internal dynamics of recurrent neural networks (RNNs) on the fly. In contrast to the conventional sequence-to-sequen…
Inferring, Predicting, and Denoising Causal Wave Dynamics
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network. It implements a grid or mesh of locally paramete…
Latent State Inference in a Spatiotemporal Generative Model
Matthias Karlbauer, Tobias Menge, Sebastian Otte +4
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions. Inferring these factors f…
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
Matthias Karlbauer, Sebastian Otte, Hendrik P. A. Lensch +3
We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA). It encodes mesh nodes using recurrent, neural prediction kernels (PKs), while neural tr…