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Found 6 papers, 2 papers with code
An analysis of Universal Differential Equations for data-driven discovery of Ordinary Differential Equations
In the last decade, the scientific community has devolved its attention to the deployment of data-driven approaches in scientific research to provide accurate and reliable analysis of a plethora of phenomena.
Deep Learning for Virus-Spreading Forecasting: a Brief Survey
The advent of the coronavirus pandemic has sparked the interest in predictive models capable of forecasting virus-spreading, especially for boosting and supporting decision-making processes.
An Analysis of Regularized Approaches for Constrained Machine Learning
Regularization-based approaches for injecting constraints in Machine Learning (ML) were introduced to improve a predictive model via expert knowledge.
Improving Deep Learning Models via Constraint-Based Domain Knowledge: a Brief Survey
Deep Learning (DL) models proved themselves to perform extremely well on a wide variety of learning tasks, as they can learn useful patterns from large data sets.
Injective Domain Knowledge in Neural Networks for Transprecision Computing
Machine Learning (ML) models are very effective in many learning tasks, due to the capability to extract meaningful information from large data sets.
Lagrangian Duality for Constrained Deep Learning
In energy domains, the combination of Lagrangian duality and deep learning can be used to obtain state-of-the-art results to predict optimal power flows, in energy systems, and optimal compressor settings, in gas networks.
