Add to ORKGPhysics-informed neural networks (PINNs) constitute a flexible approach to both finding solutions and identifying parameters of partial differential equations. Most works on the topic assume noiseless data, or data contaminated by weak Gaussian noise. We show that the standard PINN framework breaks down in case of non-Gaussian noise. We give a way of resolving this fundamental issue and we propose to jointly train an energy-based model (EBM) to learn the correct noise distribution. We illustrate the improved performance of our approach using multiple examples
In this study, we will address the problem of localising a source of pollutant given a sparse set of...
Prediction error quantification in machine learning has been left out of most methodological investi...
We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly...
Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating phy...
At the extremes, two antithetical approaches to describing natural processes exist. Theoretical mode...
Physics-informed neural networks (PINNs) have recently been used to solve various computational prob...
Physics-informed extreme learning machine (PIELM) has recently received significant attention as a r...
Physics-informed neural networks (PINNs) have recently emerged as a promising application of deep le...
The physics informed neural network (PINN) is evolving as a viable method to solve partial different...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
In order to make data-driven models of physical systems interpretable and reliable, it is essential ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
International audienceThe growing popularity of Neural Networks in computational science and enginee...
In an attempt to find alternatives for solving partial differential equations (PDEs)with traditional...
Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge ...
In this study, we will address the problem of localising a source of pollutant given a sparse set of...
Prediction error quantification in machine learning has been left out of most methodological investi...
We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly...
Physics-Informed Neural Networks (PINNs) have been shown to be an effective way of incorporating phy...
At the extremes, two antithetical approaches to describing natural processes exist. Theoretical mode...
Physics-informed neural networks (PINNs) have recently been used to solve various computational prob...
Physics-informed extreme learning machine (PIELM) has recently received significant attention as a r...
Physics-informed neural networks (PINNs) have recently emerged as a promising application of deep le...
The physics informed neural network (PINN) is evolving as a viable method to solve partial different...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
In order to make data-driven models of physical systems interpretable and reliable, it is essential ...
Physics-Informed Neural Networks (PINN) are neural networks (NNs) that encode model equations, like ...
International audienceThe growing popularity of Neural Networks in computational science and enginee...
In an attempt to find alternatives for solving partial differential equations (PDEs)with traditional...
Physics-informed neural networks (PINNs) are one popular approach to incorporate a priori knowledge ...
In this study, we will address the problem of localising a source of pollutant given a sparse set of...
Prediction error quantification in machine learning has been left out of most methodological investi...
We propose a framework and an algorithm to uncover the unknown parts of nonlinear equations directly...