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Found 7 papers, 1 papers with code
Predicting Wind-Driven Spatial Deposition through Simulated Color Images using Deep Autoencoders
We demonstrate this approach on images of spatial deposition from a pollution source, where the encoder compresses the dimensionality to 0. 02% of the original size, and the full predictive model performance on test data achieves a normalized root mean squared error of 8%, a figure of merit in space of 94% and a precision-recall area under the curve of 0. 93.
Deep Convolutional Autoencoders as Generic Feature Extractors in Seismological Applications
The idea of using a deep autoencoder to encode seismic waveform features and then use them in different seismological applications is appealing.
Uncertainty Bounds for Multivariate Machine Learning Predictions on High-Strain Brittle Fracture
Simulation of the crack network evolution on high strain rate impact experiments performed in brittle materials is very compute-intensive.
StressNet: Deep Learning to Predict Stress With Fracture Propagation in Brittle Materials
Catastrophic failure in brittle materials is often due to the rapid growth and coalescence of cracks aided by high internal stresses.
Identifying Entangled Physics Relationships through Sparse Matrix Decomposition to Inform Plasma Fusion Design
Sparse principal component analysis (SPCA) identifies groupings that are related to the physical origin of the variables (laser, hohlraum, and capsule).
Exploring Sensitivity of ICF Outputs to Design Parameters in Experiments Using Machine Learning
Identification of relationships between controllable design inputs and measurable outcomes can help guide the future design of experiments and development of simulation codes, which can potentially improve the accuracy of the computational models used to simulate ICF implosions.
Review of multi-fidelity models
Multi-fidelity models provide a framework for integrating computational models of varying complexity, allowing for accurate predictions while optimizing computational resources.
Applications 65C99, 65D15, 68W25, 76-00, 74-00 A.1; G.3; I.6.5; I.2.8
