This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series
forecasting applied to environmental datasets. The framework was applied for three different …

Across numerous applications, forecasting relies on numerical solvers for partial differential
equations (PDEs). Although the use of deep-learning techniques has been proposed, actual …

Identifying hidden dynamics from observed data is a significant and challenging task in a
wide range of applications. Recently, the combination of linear multistep methods (LMMs) …

Linear multistep methods (LMMs) are popular time discretization techniques for the
numerical solution of differential equations. Traditionally they are applied to solve for the …

In this work, we develop a deep neural network model for the reaction rate of oxidative
coupling of methane from published high-throughput experimental catalysis data. A neural …

This paper presents a novel spatio-temporal LSTM (SPATIAL) architecture for time series
forecasting applied to environmental datasets. The framework was evaluated across …

Applying deep learning methods to solve high-dimensional and nonlinear differential
equations (DE) has raised much attention recently. A goal of using machine learning in …

Prioritization of infrastructure repairs suggests a need to collect data from structures, which
contain condition information over an extended period of time. As capable sensing devices …