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Found 8 papers, 7 papers with code
A Learning-Based Optimal Uncertainty Quantification Method and Its Application to Ballistic Impact Problems
This paper concerns the study of optimal (supremum and infimum) uncertainty bounds for systems where the input (or prior) probability measure is only partially/imperfectly known (e. g., with only statistical moments and/or on a coarse topology) rather than fully specified.
Fourier Neural Operator with Learned Deformations for PDEs on General Geometries
The resulting geo-FNO model has both the computation efficiency of FFT and the flexibility of handling arbitrary geometries.
Physics-Informed Neural Operator for Learning Partial Differential Equations
Specifically, in PINO, we combine coarse-resolution training data with PDE constraints imposed at a higher resolution.
Neural Operator: Learning Maps Between Function Spaces
The classical development of neural networks has primarily focused on learning mappings between finite dimensional Euclidean spaces or finite sets.
Learning Dissipative Dynamics in Chaotic Systems
Chaotic systems are notoriously challenging to predict because of their sensitivity to perturbations and errors due to time stepping.
Fourier Neural Operator for Parametric Partial Differential Equations
The classical development of neural networks has primarily focused on learning mappings between finite-dimensional Euclidean spaces.
Multipole Graph Neural Operator for Parametric Partial Differential Equations
One of the main challenges in using deep learning-based methods for simulating physical systems and solving partial differential equations (PDEs) is formulating physics-based data in the desired structure for neural networks.
Neural Operator: Graph Kernel Network for Partial Differential Equations
The classical development of neural networks has been primarily for mappings between a finite-dimensional Euclidean space and a set of classes, or between two finite-dimensional Euclidean spaces.
