Search Results for author:
Found 8 papers, 3 papers with code
Convolutional Neural Operators for robust and accurate learning of PDEs
Although very successfully used in conventional machine learning, convolution based neural network architectures -- believed to be inconsistent in function space -- have been largely ignored in the context of learning solution operators of PDEs.
Neural Inverse Operators for Solving PDE Inverse Problems
A large class of inverse problems for PDEs are only well-defined as mappings from operators to functions.
Nonlinear Reconstruction for Operator Learning of PDEs with Discontinuities
A large class of hyperbolic and advection-dominated PDEs can have solutions with discontinuities.
wPINNs: Weak Physics informed neural networks for approximating entropy solutions of hyperbolic conservation laws
Physics informed neural networks (PINNs) require regularity of solutions of the underlying PDE to guarantee accurate approximation.
Physics Informed Neural Networks for Simulating Radiative Transfer
We propose a novel machine learning algorithm for simulating radiative transfer.
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating a class of inverse problems for PDEs
Physics informed neural networks (PINNs) have recently been very successfully applied for efficiently approximating inverse problems for PDEs.
Estimates on the generalization error of Physics Informed Neural Networks (PINNs) for approximating PDEs
Physics informed neural networks (PINNs) have recently been widely used for robust and accurate approximation of PDEs.
A Multi-level procedure for enhancing accuracy of machine learning algorithms
We propose a multi-level method to increase the accuracy of machine learning algorithms for approximating observables in scientific computing, particularly those that arise in systems modeled by differential equations.
