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Found 5 papers, 4 papers with code
Using Uncertainty Quantification to Characterize and Improve Out-of-Domain Learning for PDEs
Existing work in scientific machine learning (SciML) has shown that data-driven learning of solution operators can provide a fast approximate alternative to classical numerical partial differential equation (PDE) solvers.
Pessimistic Off-Policy Multi-Objective Optimization
The pessimistic estimator can be optimized by policy gradients and performs well in all of our experiments.
Learning Physical Models that Can Respect Conservation Laws
We provide a detailed analysis of ProbConserv on learning with the Generalized Porous Medium Equation (GPME), a widely-applicable parameterized family of PDEs that illustrates the qualitative properties of both easier and harder PDEs.
Guiding continuous operator learning through Physics-based boundary constraints
Numerical experiments based on multiple PDEs with a wide variety of applications indicate that the proposed approach ensures satisfaction of BCs, and leads to more accurate solutions over the entire domain.
Convolutional Neural Networks for Facial Expression Recognition
We have developed convolutional neural networks (CNN) for a facial expression recognition task.
