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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Embedding-driven physics informed neural network for predicting optical constants across materials [PDF]
RSC AdvancesWe introduce a deep learning framework for predicting the optical constants of materials, specifically the refractive index n(λ) and the extinction coefficient k(λ), as a function of wavelength.
Sakshi Choudhary +3 more
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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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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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Physics informed neural network consisting of two decoupled stages
Engineering Science and Technology, an International Journal, 2023 This paper proposes a two-stage physics informed neural network (PINN) along with an effective training approach for it. The first stage network output that roughly approximates the solution of a partial differential equation (PDE) is fed as input to the
Nilgun Guler Bayazit
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Investigating molecular transport in the human brain from MRI with physics-informed neural networks
Scientific Reports, 2022 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 networks, which solve problems involving partial differential equations by ...
Bastian Zapf +5 more
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DSFA-PINN: Deep Spectral Feature Aggregation Physics Informed Neural Network
IEEE Access, 2022 Solving parametric partial differential equations using artificial intelligence is taking the pace. It is primarily because conventional numerical solvers are computationally expensive and require significant time to converge a solution. However, physics
Muhammad Rafiq +2 more
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