Cardiovascular digital twins using a Windkessel physics informed neural network [PDF]
npj Digital MedicineCardiovascular digital twins (CDTs) have the potential to transform precision medicine by enabling tailored insights, continuous monitoring, and personalized simulations of cardiovascular dynamics through virtual representations of the cardiovascular ...
Deen Osman +3 more
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Inverse solution of process parameters in gear grinding using hierarchical bayesian physics informed neural network (HBPINN) [PDF]
Scientific ReportsAccurate inverse solution of process parameters by surface roughness is crucial for precision gear grinding processes. When inversely solving process parameters, model parameters are typically obtained by fitting experimental data.
Qi Zhang +5 more
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Physics-informed neural network for predicting in vacuo vocal fold eigenmodes: A proof of concept study [PDF]
JASA Express LettersThis study investigates a machine-learning approach for real-time computation of in vacuo vocal fold eigenmodes. A physics-informed neural network is trained to predict eigenmodes and eigenfrequencies by integrating the governing equations of vocal fold ...
Mohd Ethar M Al Khasawneh +2 more
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Remaining useful lifetime estimation for discrete power electronic devices using physics-informed neural network [PDF]
Scientific Reports, 2023 Estimation of Remaining Useful Lifetime (RUL) of discrete power electronics is important to enable predictive maintenance and ensure system safety. Conventional data-driven approaches using neural networks have been applied to address this challenge ...
Zhonghai Lu +3 more
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Physics-informed neural network reconciles Australian displacements and tectonic stresses [PDF]
Scientific Reports, 2023 Stress orientation information is invaluable to evaluate active tectonic forces within the Earth’s crust. The global dataset provided by the World Stress Map offers a rich resource of stress indicators, facilitating the calibration of mechanical models ...
Thomas Poulet, Pouria Behnoudfar
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Physics-informed neural network simulation of thermal cavity flow [PDF]
Scientific ReportsPhysics-informed neural networks (PINNs) are an emerging technology that can be used both in place of and in conjunction with conventional simulation methods. In this paper, we used PINNs to perform a forward simulation without leveraging known data. Our
Eric Fowler +2 more
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PDE-NetGen 1.0: from symbolic partial differential equation (PDE) representations of physical processes to trainable neural network representations [PDF]
Geoscientific Model Development, 2020 Bridging physics and deep learning is a topical challenge. While deep learning frameworks open avenues in physical science, the design of physically consistent deep neural network architectures is an open issue.
O. Pannekoucke, R. Fablet
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Conditional physics informed neural networks
Communications in Nonlinear Science and Numerical Simulation, 2022 18 pages, 11 ...
Alexander Kovacs +13 more
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Separable Physics-Informed Neural Networks
Advances in Neural Information Processing Systems 36, 2023 Physics-informed neural networks (PINNs) have recently emerged as promising data-driven PDE solvers showing encouraging results on various PDEs. However, there is a fundamental limitation of training PINNs to solve multi-dimensional PDEs and approximate highly complex solution functions.
Junwoo Cho +5 more
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Journal of Petroleum Exploration and Production Technology, 2023 Efficient and accurate evaluation of capillary pressure and relative permeability of oil–water flow in tight sandstone with limited routinely obtainable parameters is a crucial problem in tight oil reservoir modeling and petroleum engineering. Due to the
Lili Ji +6 more
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