Respecting causality is all you need for training physics-informed neural networks
While the popularity of physics-informed neural networks (PINNs) is steadily rising, to this
date PINNs have not been successful in simulating dynamical systems whose solution …
date PINNs have not been successful in simulating dynamical systems whose solution …
Physics-informed neural networks for inverse problems in supersonic flows
Accurate solutions to inverse supersonic compressible flow problems are often required for
designing specialized aerospace vehicles. In particular, we consider the problem where we …
designing specialized aerospace vehicles. In particular, we consider the problem where we …
Reliable extrapolation of deep neural operators informed by physics or sparse observations
Deep neural operators can learn nonlinear mappings between infinite-dimensional function
spaces via deep neural networks. As promising surrogate solvers of partial differential …
spaces via deep neural networks. As promising surrogate solvers of partial differential …
A critical review of physics-informed machine learning applications in subsurface energy systems
Abstract Machine learning has emerged as a powerful tool in various fields, including
computer vision, natural language processing, and speech recognition. It can unravel …
computer vision, natural language processing, and speech recognition. It can unravel …
Diagnostic strategies for breast cancer detection: from image generation to classification strategies using artificial intelligence algorithms
JA Basurto-Hurtado, IA Cruz-Albarran… - Cancers, 2022 - mdpi.com
Simple Summary With the recent advances in the field of artificial intelligence, it has been
possible to develop robust and accurate methodologies that can deliver noticeable results in …
possible to develop robust and accurate methodologies that can deliver noticeable results in …
How important are activation functions in regression and classification? A survey, performance comparison, and future directions
AD Jagtap, GE Karniadakis - Journal of Machine Learning for …, 2023 - dl.begellhouse.com
Inspired by biological neurons, the activation functions play an essential part in the learning
process of any artificial neural network (ANN) commonly used in many real-world problems …
process of any artificial neural network (ANN) commonly used in many real-world problems …
[HTML][HTML] Augmented Physics-Informed Neural Networks (APINNs): A gating network-based soft domain decomposition methodology
Abstract Physics-Informed Neural Networks (PINNs) and extended PINNs (XPINNs) have
emerged as a promising approach in computational science and engineering for solving …
emerged as a promising approach in computational science and engineering for solving …
Physics-informed attention-based neural network for hyperbolic partial differential equations: application to the Buckley–Leverett problem
R Rodriguez-Torrado, P Ruiz, L Cueto-Felgueroso… - Scientific reports, 2022 - nature.com
Physics-informed neural networks (PINNs) have enabled significant improvements in
modelling physical processes described by partial differential equations (PDEs) and are in …
modelling physical processes described by partial differential equations (PDEs) and are in …
The cost-accuracy trade-off in operator learning with neural networks
The termsurrogate modeling'in computational science and engineering refers to the
development of computationally efficient approximations for expensive simulations, such as …
development of computationally efficient approximations for expensive simulations, such as …
Deep learning of inverse water waves problems using multi-fidelity data: Application to Serre–Green–Naghdi equations
We consider strongly-nonlinear and weakly-dispersive surface water waves governed by
equations of Boussinesq type, known as the Serre–Green–Naghdi system; it describes …
equations of Boussinesq type, known as the Serre–Green–Naghdi system; it describes …