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Showing 1 to 6 of 6 for “"neural ordinary differential equations"”.
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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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Graph-based Multi-ODE Neural Networks for Spatio-Temporal Traffic Forecasting
… structures and learn representations using graph neural networks (GNNs), but this approach suffers from over-smoothing problem in deep architectures. To tackle this problem, recent methods introduced the combination of GNNs with residual connections or neural ordinary differential equations …
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Optimization methods for parameter identifications in settings with only partial knowledge
… Models (ROMs) with Physics-Informed Neural Ordinary Differential Equations (PINODE). In particular, a classic technique of collocation points is adapted to transfer knowledge from a known equation to a model that approximates solutions of that equation. The addition of a …
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Advanced Reconstruction Techniques for CUORE: Searching Beyond the Standard Model with Cryogenic Calorimeters
… methods for the end-to-end training of neural ordinary differential equations to model thermal pulse dynamics within CUORE calorimeter channels. These methods and results improve understanding of detector response, enable improved in situ background characterization, and open novel …
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Physics-guided Machine Learning for Condition Assessment of Building Structures in Operational Environments
… (FRF) data to pre-train deep convolutional neural networks (CNNs) and fine-tune them using limited real-world measurements, significantly improving damage localisation and severity identification. Additionally, a Joint Maximum Discrepancy and Adversarial Discriminative Domain Adaptation …
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Meta-learning representations with relational structure
… relational inference to modulate and recombine neural modules for fast and accurate adaptation at test time. And finally, adapting Neural Processes to capture relational and temporal dependencies is shown to improve the accuracy and coherency of predictions and uncertainty estimates. In …