目录
- Mechanistic Machine Learning: Theory, Methods, and Applications
- Deep density estimation via invertible block-triangular mapping
- Classifying wakes produced by self-propelled fish-like swimmers using neural networks
- Physics-constrained indirect supervised learning
- Physics-constrained bayesian neural network for fluid flow reconstruction with sparse and noisy data
- Reducing parameter space for neural network training
- Nonnegativity-enforced Gaussian process regression
- A perspective on regression and Bayesian approaches for system identification of pattern formation dynamics
- Multi-fidelity Gaussian process based empirical potential development for Si:H nanowires
- Learning material law from displacement fields by artificial neural network
- Physics-informed deep learning for incompressible laminar flows