scientific machine learning
Scientific machine learning pertains to the intersection of machine learning and scientific computing, emphasizing the use of data-driven algorithms to solve complex scientific problems, facilitating discovery in various disciplines through enhanced predictive modeling and simulation.
- A Plug-and-Play Query Synthesis Active Learning Framework for Neural PDE Solvers
- Collapsing Taylor Mode Automatic Differentiation
- PDEfuncta: Spectrally-Aware Neural Representation for PDE Solution Modeling
- PINN Balls: Scaling Second-Order Methods for PINNs with Domain Decomposition and Adaptive Sampling
- Uncertainty Quantification for Physics-Informed Neural Networks with Extended Fiducial Inference
- UniFoil: A Universal Dataset of Airfoils in Transitional and Turbulent Regimes for Subsonic and Transonic Flows