Physics-informed neural network (PINN) evolution and beyond: A systematic literature review and bibliometric analysis
This research aims to study and assess state-of-the-art physics-informed neural networks
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …
(PINNs) from different researchers' perspectives. The PRISMA framework was used for a …
Investigating molecular transport in the human brain from MRI with physics-informed neural networks
In recent years, a plethora of methods combining neural networks and partial differential
equations have been developed. A widely known example are physics-informed neural …
equations have been developed. A widely known example are physics-informed neural …
Extreme theory of functional connections: A fast physics-informed neural network method for solving ordinary and partial differential equations
We present a novel, accurate, fast, and robust physics-informed neural network method for
solving problems involving differential equations (DEs), called Extreme Theory of Functional …
solving problems involving differential equations (DEs), called Extreme Theory of Functional …
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 …
A novel numerical approach for time-varying impulsive fractional differential equations using theory of functional connections and neural network
SM Sivalingam, V Govindaraj - Expert Systems with Applications, 2024 - Elsevier
In this paper, we propose a physics-informed neural network-based scheme to solve time-
varying impulsive fractional differential equations without any labeled data. At first, the …
varying impulsive fractional differential equations without any labeled data. At first, the …
Physics-informed neural networks and functional interpolation for stiff chemical kinetics
This work presents a recently developed approach based on physics-informed neural
networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical …
networks (PINNs) for the solution of initial value problems (IVPs), focusing on stiff chemical …
Physics-informed neural networks for rarefied-gas dynamics: Thermal creep flow in the Bhatnagar–Gross–Krook approximation
This work aims at accurately solve a thermal creep flow in a plane channel problem, as a
class of rarefied-gas dynamics problems, using Physics-Informed Neural Networks (PINNs) …
class of rarefied-gas dynamics problems, using Physics-Informed Neural Networks (PINNs) …
AI-Aristotle: A physics-informed framework for systems biology gray-box identification
N Ahmadi Daryakenari, M De Florio… - PLOS Computational …, 2024 - journals.plos.org
Discovering mathematical equations that govern physical and biological systems from
observed data is a fundamental challenge in scientific research. We present a new physics …
observed data is a fundamental challenge in scientific research. We present a new physics …
Physics-informed neural networks for optimal planar orbit transfers
This paper presents a novel framework, combining the indirect method and Physics-
Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal …
Informed Neural Networks (PINNs), to learn optimal control actions for a series of optimal …
[HTML][HTML] Nonlinear discrete-time observers with physics-informed neural networks
HV Alvarez, G Fabiani, N Kazantzis… - Chaos, Solitons & …, 2024 - Elsevier
We use physics-informed neural networks (PINNs) to numerically solve the discrete-time
nonlinear observer-based state estimation problem. Integrated within a single-step exact …
nonlinear observer-based state estimation problem. Integrated within a single-step exact …