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Found 9 papers, 4 papers with code
Advancing Parsimonious Deep Learning Weather Prediction using the HEALPix Mesh
We present a parsimonious deep learning weather prediction model on the Hierarchical Equal Area isoLatitude Pixelization (HEALPix) to forecast seven atmospheric variables for arbitrarily long lead times on a global approximately 110 km mesh at 3h time resolution.
Inductive biases in deep learning models for weather prediction
We show how the design choices made in each of the five design elements relate to structural assumptions.
Learning What and Where: Disentangling Location and Identity Tracking Without Supervision
Moreover, it can anticipate object motion and interactions, which are crucial abilities for conceptual planning and reasoning.
Ranked #1 on
Video Object Tracking
on CATER
Composing Partial Differential Equations with Physics-Aware Neural Networks
We introduce a compositional physics-aware FInite volume Neural Network (FINN) for learning spatiotemporal advection-diffusion processes.
Finite Volume Neural Network: Modeling Subsurface Contaminant Transport
To tackle this issue, we introduce a new approach called the Finite Volume Neural Network (FINN).
Active Tuning
We introduce Active Tuning, a novel paradigm for optimizing the internal dynamics of recurrent neural networks (RNNs) on the fly.
Latent State Inference in a Spatiotemporal Generative Model
Knowledge about the hidden factors that determine particular system dynamics is crucial for both explaining them and pursuing goal-directed interventions.
Inferring, Predicting, and Denoising Causal Wave Dynamics
The novel DISTributed Artificial neural Network Architecture (DISTANA) is a generative, recurrent graph convolution neural network.
A Distributed Neural Network Architecture for Robust Non-Linear Spatio-Temporal Prediction
We introduce a distributed spatio-temporal artificial neural network architecture (DISTANA).
