Emerging opportunities and challenges for the future of reservoir computing
Reservoir computing originates in the early 2000s, the core idea being to utilize dynamical
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
systems as reservoirs (nonlinear generalizations of standard bases) to adaptively learn …
Gnot: A general neural operator transformer for operator learning
Learning partial differential equations'(PDEs) solution operators is an essential problem in
machine learning. However, there are several challenges for learning operators in practical …
machine learning. However, there are several challenges for learning operators in practical …
A unified scalable framework for causal sweeping strategies for physics-informed neural networks (PINNs) and their temporal decompositions
Physics-informed neural networks (PINNs) as a means of solving partial differential
equations (PDE) have garnered much attention in the Computational Science and …
equations (PDE) have garnered much attention in the Computational Science and …
[HTML][HTML] Tackling the curse of dimensionality with physics-informed neural networks
The curse-of-dimensionality taxes computational resources heavily with exponentially
increasing computational cost as the dimension increases. This poses great challenges in …
increasing computational cost as the dimension increases. This poses great challenges in …
A taxonomic survey of physics-informed machine learning
Physics-informed machine learning (PIML) refers to the emerging area of extracting
physically relevant solutions to complex multiscale modeling problems lacking sufficient …
physically relevant solutions to complex multiscale modeling problems lacking sufficient …
[HTML][HTML] Multilevel domain decomposition-based architectures for physics-informed neural networks
Physics-informed neural networks (PINNs) are a powerful approach for solving problems
involving differential equations, yet they often struggle to solve problems with high frequency …
involving differential equations, yet they often struggle to solve problems with high frequency …
[HTML][HTML] Hutchinson trace estimation for high-dimensional and high-order physics-informed neural networks
Abstract Physics-Informed Neural Networks (PINNs) have proven effective in solving partial
differential equations (PDEs), especially when some data are available by seamlessly …
differential equations (PDEs), especially when some data are available by seamlessly …
Enhancing training of physics-informed neural networks using domain decomposition–based preconditioning strategies
We propose to enhance the training of physics-informed neural networks. To this aim, we
introduce nonlinear additive and multiplicative preconditioning strategies for the widely used …
introduce nonlinear additive and multiplicative preconditioning strategies for the widely used …
Self-adaptive physics-driven deep learning for seismic wave modeling in complex topography
Y Ding, S Chen, X Li, S Wang, S Luan, H Sun - Engineering Applications of …, 2023 - Elsevier
Solving for the scattered wavefield is a key scientific problem in the field of seismology and
earthquake engineering. Physics-informed neural networks (PINNs) developed in recent …
earthquake engineering. Physics-informed neural networks (PINNs) developed in recent …
Physics-informed neural network-based surrogate model for a virtual thermal sensor with real-time simulation
In this study, a physics-informed neural network (PINN)-based surrogate model was
proposed for a virtual thermal sensor (VTS) with real-time simulation. This surrogate model …
proposed for a virtual thermal sensor (VTS) with real-time simulation. This surrogate model …