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Found 3 papers, 2 papers with code
A spatio-temporal LSTM model to forecast across multiple temporal and spatial scales
Enabling learning across the spatial and temporal directions, this paper addresses two fundamental challenges of ML applications to environmental science: 1) data sparsity and the challenges and costs of collecting measurements of environmental conditions such as ocean dynamics, and 2) environmental datasets are inherently connected in the spatial and temporal directions while classical ML approaches only consider one of these directions.
Neural Time-Dependent Partial Differential Equation
Inspired by the traditional finite difference and finite elements methods and emerging advancements in machine learning, we propose a sequence-to-sequence learning (Seq2Seq) framework called Neural-PDE, which allows one to automatically learn governing rules of any time-dependent PDE system from existing data by using a bidirectional LSTM encoder, and predict the solutions in next $n$ time steps.
Neural-PDE: A RNN based neural network for solving time dependent PDEs
Partial differential equations (PDEs) play a crucial role in studying a vast number of problems in science and engineering.
