Physics-informed machine learning
Despite great progress in simulating multiphysics problems using the numerical
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
discretization of partial differential equations (PDEs), one still cannot seamlessly incorporate …
Free-form optimization of nanophotonic devices: from classical methods to deep learning
Nanophotonic devices have enabled microscopic control of light with an unprecedented
spatial resolution by employing subwavelength optical elements that can strongly interact …
spatial resolution by employing subwavelength optical elements that can strongly interact …
Probing the solar coronal magnetic field with physics-informed neural networks
R Jarolim, JK Thalmann, AM Veronig… - Nature …, 2023 - nature.com
While the photospheric magnetic field of our Sun is routinely measured, its extent into the
upper atmosphere is typically not accessible by direct observations. Here we present an …
upper atmosphere is typically not accessible by direct observations. Here we present an …
A method for computing inverse parametric PDE problems with random-weight neural networks
We present a method for computing the inverse parameters and the solution field to inverse
parametric partial differential equations (PDE) based on randomized neural networks. This …
parametric partial differential equations (PDE) based on randomized neural networks. This …
On Training Derivative-Constrained Neural Networks
KC Lo, D Huang - arXiv preprint arXiv:2310.01649, 2023 - arxiv.org
We refer to the setting where the (partial) derivatives of a neural network's (NN's) predictions
with respect to its inputs are used as additional training signal as a derivative-constrained …
with respect to its inputs are used as additional training signal as a derivative-constrained …
Quantifying experimental edge plasma evolution via multidimensional adaptive Gaussian process regression
The edge density and temperature of tokamak plasmas are strongly correlated with energy
and particle confinement and their quantification is fundamental to understanding edge …
and particle confinement and their quantification is fundamental to understanding edge …
[PDF][PDF] Artificial Neutral Networks (ANNs) Applied as CFD 0ptimization Techniques
I Sadrehaghighi - 2021 - academia.edu
Time-varying fluid flows are ubiquitous in modern engineering and in the life sciences.
Particularly challenging is the characterization of unsteady aerodynamic forces and …
Particularly challenging is the characterization of unsteady aerodynamic forces and …
[引用][C] Physics-Informed Spatio-Temporal Graph Convolutional Network for Interpretable Pedestrian Trajectory Prediction
R Guo, Z Zhu, Y Zhao, B Chen