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Quantum Physics-Informed Neural Networks [PDF]
EntropyIn this study, the PennyLane quantum device simulator was used to investigate quantum and hybrid, quantum/classical physics-informed neural networks (PINNs) for solutions to both transient and steady-state, 1D and 2D partial differential equations.
Corey Trahan, Mark Loveland, Samuel Dent
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Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks [PDF]
Heliyon, 2023 In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs).
S. Berrone +3 more
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Protein Design Using Physics Informed Neural Networks
Biomolecules, 2023 The inverse protein folding problem, also known as protein sequence design, seeks to predict an amino acid sequence that folds into a specific structure and performs a specific function.
Sara Ibrahim Omar +3 more
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Automatic network structure discovery of physics informed neural networks via knowledge distillation [PDF]
Nature CommunicationsPartial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws.
Ziti Liu +6 more
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Three-Dimensional Thermal Tomography with Physics-Informed Neural Networks [PDF]
TomographyBackground: Accurate reconstruction of internal temperature fields from surface temperature data is critical for applications such as non-invasive thermal imaging, particularly in scenarios involving small temperature gradients, like those in the human ...
Theodoros Leontiou +6 more
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Hamiltonian learning via inverse physics-informed neural networks
Physical Review ResearchHamiltonian learning (HL), enabling precise estimation of system parameters and underlying dynamics, plays a critical role in characterizing quantum systems.
Jie Liu, Xin Wang
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Scalable algorithms for physics-informed neural and graph networks
Data-Centric Engineering, 2022 Physics-informed machine learning (PIML) has emerged as a promising new approach for simulating complex physical and biological systems that are governed by complex multiscale processes for which some data are also available.
Khemraj Shukla +3 more
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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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Physics informed neural networks for triple deck [PDF]
Aircraft Engineering and Aerospace Technology, 2022 Purpose This paper aims to introduce physics-informed neural networks (PINN) applied to the two-dimensional steady-state laminar Navier–Stokes equations over a flat plate with roughness elements and specified local heating. The method bridges the gap between asymptotics theory and three-dimensional turbulent flow analyses, characterized by high costs ...
Abderrahmane, Belkallouche +3 more
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fPINNs: Fractional Physics-Informed Neural Networks [PDF]
SIAM Journal on Scientific Computing, 2019 Physics-informed neural networks (PINNs) are effective in solving integer-order partial differential equations (PDEs) based on scattered and noisy data. PINNs employ standard feedforward neural networks (NNs) with the PDEs explicitly encoded into the NN using automatic differentiation, while the sum of the mean-squared PDE-residuals and the mean ...
Guofei Pang +2 more
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