A physics-informed neural network based simulation tool for reacting flow with multicomponent reactants

Physics-informed neural networks (PINNs) for fluid mechanics: a review

(2022) Shengze Cai et al.   ACTA MECHANICA SINICA

Gradient-enhanced physics-informed neural networks for forward and inverse PDE problems

(2022) Jeremy Yu et al.   COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING

Physics‐Informed Neural Network for Nonlinear Dynamics in Fiber Optics

(2022) Xiaotian Jiang et al.   Laser & Photonics Reviews

Orthogonal grid physics-informed neural networks: A neural network-based simulation tool for advection–diffusion–reaction problems

(2022) Qingzhi Hou et al.   PHYSICS OF FLUIDS

Deep learning paradigm for prediction of stress distribution in damaged structural components with stress concentrations

(2022) Hamed Bolandi et al.   ADVANCES IN ENGINEERING SOFTWARE

Flow over an espresso cup: inferring 3-D velocity and pressure fields from tomographic background oriented Schlieren via physics-informed neural networks

(2021) Shengze Cai et al.   JOURNAL OF FLUID MECHANICS

Sentiment Analysis of Students’ Feedback with NLP and Deep Learning: A Systematic Mapping Study

(2021) Zenun Kastrati et al.   Applied Sciences-Basel

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

Data-driven femtosecond optical soliton excitations and parameters discovery of the high-order NLSE using the PINN

(2021) Yin Fang et al.   NONLINEAR DYNAMICS

A nonlocal physics-informed deep learning framework using the peridynamic differential operator

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

Deep Learning for Natural Language Processing in Radiology—Fundamentals and a Systematic Review

(2020) Vera Sorin et al.   Journal of the American College of Radiology

Deep learning-based method coupled with small sample learning for solving partial differential equations

(2020) Ying Li et al.   MULTIMEDIA TOOLS AND APPLICATIONS

A Lagrangian Particle Algorithm (SPH) for an Autocatalytic Reaction Model with Multicomponent Reactants

(2019) Qingzhi Hou et al.   Processes

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

Error bounds for approximations with deep ReLU networks

(2017) Dmitry Yarotsky   NEURAL NETWORKS

Mathematical Model for Pollution Source Identification in Rivers

(2015) Mehdi Mazaheri et al.   ENVIRONMENTAL FORENSICS

Identification of multiple moving pollution sources in surface waters or atmospheric media with boundary observations

(2012) M Andrle et al.   INVERSE PROBLEMS

Identification of moving pointwise sources in an advection–dispersion–reaction equation

(2011) M Andrle et al.   INVERSE PROBLEMS

Identification of contaminant point source in surface waters based on backward location probability density function method

(2010) Wei Ping Cheng et al.   ADVANCES IN WATER RESOURCES