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Found 11 papers, 5 papers with code
Recency-Weighted Temporally-Segmented Ensemble for Time-Series Modeling
Addressing this, we introduce the Recency-Weighted Temporally-Segmented (ReWTS, pronounced `roots') ensemble model, a novel chunk-based approach for multi-step forecasting.
Road Graph Generator: Mapping roads at construction sites from GPS data
We propose a new method for inferring roads from GPS trajectories to map construction sites.
Balancing the Norwegian regulated power market anno 2016 to 2022
However, the volumes and share of balancing power compared to overall production have increased, suggesting that the hours which are inherently difficult to predict remain the same.
DON-LSTM: Multi-Resolution Learning with DeepONets and Long Short-Term Memory Neural Networks
Deep operator networks (DeepONets, DONs) offer a distinct advantage over traditional neural networks in their ability to be trained on multi-resolution data.
Pseudo-Hamiltonian system identification
Identifying the underlying dynamics of physical systems can be challenging when only provided with observational data.
Neural Operator Learning for Long-Time Integration in Dynamical Systems with Recurrent Neural Networks
Deep neural networks are an attractive alternative for simulating complex dynamical systems, as in comparison to traditional scientific computing methods, they offer reduced computational costs during inference and can be trained directly from observational data.
Pseudo-Hamiltonian Neural Networks with State-Dependent External Forces
Hybrid machine learning based on Hamiltonian formulations has recently been successfully demonstrated for simple mechanical systems, both energy conserving and not energy conserving.
Mutual information estimation for graph convolutional neural networks
Mutual information can be used as a measure of the quality of internal representations in deep learning models, and the information plane may provide insights into whether the model exploits the available information in the data.
A Levenberg-Marquardt algorithm for sparse identification of dynamical systems
Low complexity of a system model is essential for its use in real-time applications.
Inferring feature importance with uncertainties in high-dimensional data
We build upon the recently published feature importance measure of SAGE (Shapley additive global importance) and introduce sub-SAGE which can be estimated without resampling for tree-based models.
Data-driven prediction of vortex-induced vibration response of marine risers subjected to three-dimensional current
The load model and hydrodynamic parameters in present VIV prediction tools are developed based on two-dimensional (2D) flow conditions, as it is challenging to consider the effect of 3D flow along the risers.
