A novel key performance analysis method for permanent magnet coupler using physics-informed neural networks

Boundedness of solutions to Dirichlet, Neumann and Robin problems for elliptic equations in Orlicz spaces

(2023) Giuseppina Barletta et al.   CALCULUS OF VARIATIONS AND PARTIAL DIFFERENTIAL EQUATIONS

Physics-informed neural energy-force network: a unified solver-free numerical simulation for structural optimization

(2023) Hau T. Mai et al.   ENGINEERING WITH COMPUTERS

Meshless methods for American option pricing through Physics-Informed Neural Networks

(2023) Federico Gatta et al.   ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS

Analyses of internal structures and defects in materials using physics-informed neural networks

(2022) Enrui Zhang et al.   Science Advances

Physics-informed deep learning for solving phonon Boltzmann transport equation with large temperature non-equilibrium

(2022) Ruiyang Li et al.   npj Computational Materials

Physics informed neural networks for control oriented thermal modeling of buildings

(2022) Gargya Gokhale et al.   APPLIED ENERGY

A physics-informed variational DeepONet for predicting crack path in quasi-brittle materials

(2022) Somdatta Goswami et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

On the application of physics informed neural networks (PINN) to solve boundary layer thermal-fluid problems

(2022) Hassan Bararnia et al.   INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER

Transfer physics informed neural network: a new framework for distributed physics informed neural networks via parameter sharing

(2022) Sreehari Manikkan et al.   ENGINEERING WITH COMPUTERS

A physics-informed neural network-based surrogate framework to predict moisture concentration and shrinkage of a plant cell during drying

(2022) C.P. Batuwatta-Gamage et al.   JOURNAL OF FOOD ENGINEERING

A physics-informed neural network technique based on a modified loss function for computational 2D and 3D solid mechanics

(2022) Jinshuai Bai et al.   COMPUTATIONAL MECHANICS

Physics-Informed Neural Networks for Solving Parametric Magnetostatic Problems

(2022) Andres Beltran-Pulido et al.   IEEE TRANSACTIONS ON ENERGY CONVERSION

Physics-informed deep learning approach for modeling crustal deformation

(2022) Tomohisa Okazaki et al.   Nature Communications

Data-driven physics-informed constitutive metamodeling of complex fluids: A multifidelity neural network (MFNN) framework

(2021) Mohammadamin Mahmoudabadbozchelou et al.   JOURNAL OF RHEOLOGY

Physics-Informed Neural Networks for Heat Transfer Problems

(2021) Shengze Cai et al.   JOURNAL OF HEAT TRANSFER-TRANSACTIONS OF THE ASME

On the potential of hard ferrite ceramics for permanent magnet technology—a review on sintering strategies

(2021) Cecilia Granados-Miralles et al.   JOURNAL OF PHYSICS D-APPLIED PHYSICS

A physics-informed deep learning framework for inversion and surrogate modeling in solid mechanics

(2021) Ehsan Haghighat et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Analysis of an IPMSM Hybrid Magnetic Equivalent Circuit

(2021) In-Soo Song et al.   Energies

Estimates on the generalization error of physics-informed neural networks for approximating a class of inverse problems for PDEs

(2021) Siddhartha Mishra et al.   IMA JOURNAL OF NUMERICAL ANALYSIS

Meshless Physics‐Informed Deep Learning Method for Three‐Dimensional Solid Mechanics

(2021) Diab W. Abueidda et al.   INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING

A Generic 3-D Analytical Model of Permanent Magnet Eddy-Current Couplings Using a Magnetic Vector Potential Formulation

(2021) Jian Wang   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Design and Optimization of Coaxial Reluctance Magnetic Gear With Different Rotor Topologies

(2021) Seyed Ahmadreza Afsari Kashani   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Hidden fluid mechanics: Learning velocity and pressure fields from flow visualizations

(2020) Maziar Raissi et al.   SCIENCE

Physics-informed neural networks for inverse problems in nano-optics and metamaterials

(2020) Yuyao Chen et al.   OPTICS EXPRESS

Analytical Modeling of an Axial Field Magnetic Coupler With Cylindrical Magnets

(2020) L. Belguerras et al.   IEEE TRANSACTIONS ON MAGNETICS

NSFnets (Navier-Stokes flow nets): Physics-informed neural networks for the incompressible Navier-Stokes equations

(2020) Xiaowei Jin et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data

(2019) Luning Sun et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Adaptive activation functions accelerate convergence in deep and physics-informed neural networks

(2019) Ameya D. Jagtap et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Machine learning in cardiovascular flows modeling: Predicting arterial blood pressure from non-invasive 4D flow MRI data using physics-informed neural networks

(2019) Georgios Kissas et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Multi-Physics Design of a V-shape IPM Motor

(2018) Paul Akiki et al.   IEEE TRANSACTIONS ON ENERGY CONVERSION

A Simple Method for Performance Prediction of Permanent Magnet Eddy Current Couplings Using a New Magnetic Equivalent Circuit Model

(2018) Jian Wang et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Method of Fundamental Solutions Applied to 3-D Velocity Induced Eddy Current Problems

(2018) Bojana Petkovic et al.   IEEE TRANSACTIONS ON MAGNETICS

Accurate Prediction and Analysis of Electromagnetic Fields and Forces in Flux-Focusing Eddy Current Coupling With Double Slotted Conductor Rotors

(2018) Zhao Li et al.   IEEE Access

Deep UQ: Learning deep neural network surrogate models for high dimensional uncertainty quantification

(2018) Rohit K. Tripathy et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Solving high-dimensional partial differential equations using deep learning

(2018) Jiequn Han et al.   PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA

Deep learning of vortex-induced vibrations

(2018) Maziar Raissi et al.   JOURNAL OF FLUID MECHANICS

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

(2018) M. Raissi et al.   JOURNAL OF COMPUTATIONAL PHYSICS

Design Optimization of Double-Sided Permanent-Magnet Axial Eddy-Current Couplers for Use in Dynamic Applications

(2018) Vahid Aberoomand et al.   IEEE TRANSACTIONS ON ENERGY CONVERSION

Computationally Efficient Analysis of Double PM-Rotor Radial-Flux Eddy Current Couplers

(2017) Abram S. Erasmus et al.   IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS

Design of an Axial Flux Permanent Magnet Synchronous Machine Using Analytical Method and Evolutionary Optimization

(2016) Peter Virtic et al.   IEEE TRANSACTIONS ON ENERGY CONVERSION

Analytical Modeling of Axial-Flux Permanent Magnet Eddy Current Couplings With a Slotted Conductor Topology

(2016) Xin Dai et al.   IEEE TRANSACTIONS ON MAGNETICS

Steady-State and Transient Performance of Axial-Field Eddy-Current Coupling

(2015) Thierry Lubin et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

3-D Analytical Model for Axial-Flux Eddy-Current Couplings and Brakes Under Steady-State Conditions

(2015) Thierry Lubin et al.   IEEE TRANSACTIONS ON MAGNETICS

Deep learning

(2015) Yann LeCun et al.   NATURE

Nonlinear Modeling of Eddy-Current Couplers

(2014) Sajjad Mohammadi et al.   IEEE TRANSACTIONS ON ENERGY CONVERSION

Double-sided permanent-magnet radial-flux eddy-current couplers: three-dimensional analytical modelling, static and transient study, and sensitivity analysis

(2013) Sajjad Mohammadi et al.   IET Electric Power Applications

Development and Analysis of Tubular Transverse Flux Machine With Permanent-Magnet Excitation

(2011) Jibin Zou et al.   IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS

Linear Induction Motors with Modular Winding Primaries and Wound Rotor Secondaries

(2008) F. Eastham et al.   IEEE TRANSACTIONS ON MAGNETICS