Global ETD Search
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Showing 1 to 5 of 5 for “"Neural ODE"”.
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On Efficient Training & Inference of Neural Differential Equations
… that automatically adapt to new problems. Neural Differential Equations have emerged as a popular modeling framework, enabling ML practitioners to design neural networks that can adaptively modify their depth based on the input problem. Neural Differential Equations combine differential …
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Machine learning applications in astrophysics: Reduced-order modelling for chemical kinetics and galaxy merger reconstruction with graph neural network
… chemistry in simulations. The combination of neural ordinary differential equations and autoencoders is shown to be a promising approach for reducing the complexity of chemical kinetics simulations. Specifically, autoencoders identify the reduced reaction subspace while the neural ODE learn …
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Neural network enhanced off-road skid-steer vehicle modeling with an application to path planning
Submission published under a 24 month embargo labeled 'Closed Access', the embargo will last until 2024-08-01
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Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
… the development of spatio-temporal forecasting models in many applications, and traffic forecasting is one of the most important ones. Long-range traffic forecasting, however, remains a challenging task due to the intricate and extensive spatio-temporal correlations observed in traffic networks. …
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Continuous-time Latent-variable Models for Time Series
… approaches for time series is latent-variable models, thanks to their ability to handle multi-dimensional data with complex interactions. Typically, these models represent a timeline as a sequence of discrete states and therefore assume that observations occur at regular intervals. However, this …