Search Results for author:
Found 5 papers, 3 papers with code
Invertible Fourier Neural Operators for Tackling Both Forward and Inverse Problems
In this paper, we propose an invertible Fourier Neural Operator (iFNO) that tackles both the forward and inverse problems.
Solving High Frequency and Multi-Scale PDEs with Gaussian Processes
Machine learning based solvers have garnered much attention in physical simulation and scientific computing, with a prominent example, physics-informed neural networks (PINNs).
Equation Discovery with Bayesian Spike-and-Slab Priors and Efficient Kernels
To overcome the computational challenge of kernel regression, we place the function values on a mesh and induce a Kronecker product construction, and we use tensor algebra to enable efficient computation and optimization.
A Kernel Approach for PDE Discovery and Operator Learning
This article presents a three-step framework for learning and solving partial differential equations (PDEs) using kernel methods.
AutoIP: A United Framework to Integrate Physics into Gaussian Processes
Physical modeling is critical for many modern science and engineering applications.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
