Neural-physics multi-fidelity model with active learning and uncertainty quantification for GPU-enabled microfluidic concentration gradient generator design
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Publication:6153894
DOI10.1016/j.cma.2023.116434OpenAlexW4386947672MaRDI QIDQ6153894
No author found.
Publication date: 14 February 2024
Published in: Computer Methods in Applied Mechanics and Engineering (Search for Journal in Brave)
Full work available at URL: https://doi.org/10.1016/j.cma.2023.116434
neural networkactive learninguncertainty quantificationGPU computingmulti-fidelitymicrofluidic concentration gradient generator
Cites Work
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- Multi-fidelity deep neural network surrogate model for aerodynamic shape optimization
- Aleatoric and epistemic uncertainty in machine learning: an introduction to concepts and methods
- Active learning Bayesian support vector regression model for global approximation
- A composite neural network that learns from multi-fidelity data: application to function approximation and inverse PDE problems
- Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization
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