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Quantum Physics-Informed Neural Networks [PDF]

Entropy
In 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
doaj   +3 more sources

CoRoPINN: Cognitive region optimized physics-informed neural networks. [PDF]

PLoS ONE
The solution of partial differential equations (PDEs) remains a significant problem in scientific computing. Although physics-informed neural networks (PINNs) provide a mesh-free paradigm, pointwise constraints offer limited supervision between ...
Jingcong Li, Yu Liao, Ximeng Wang, Jie Deng   +3 more
doaj   +2 more sources

Automatic network structure discovery of physics informed neural networks via knowledge distillation [PDF]

Nature Communications
Partial differential equations (PDEs) are fundamental for modeling complex physical processes, often exhibiting structural features such as symmetries and conservation laws.
Ziti Liu, Yang Liu, Xunshi Yan, Wen Liu, Han Nie, Shuaiqi Guo, Chen-an Zhang   +6 more
doaj   +2 more sources

Physics‐Informed Neural Networks to Model and Control Robots: A Theoretical and Experimental Investigation

Advanced Intelligent Systems
This work concerns the application of physics‐informed neural networks to the modeling and control of complex robotic systems. Achieving this goal requires extending physics‐informed neural networks to handle nonconservative effects. These learned models
Jingyue Liu, Pablo Borja, Cosimo Della Santina   +2 more
doaj   +2 more sources

Enforcing Dirichlet boundary conditions in physics-informed neural networks and variational physics-informed neural networks

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, C. Canuto, M. Pintore, N. Sukumar   +3 more
doaj   +1 more source

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, Chen Keasar, Ariel J. Ben-Sasson, Eldad Haber   +3 more
doaj   +1 more source

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, Mengjia Xu, Nathaniel Trask, George E. Karniadakis   +3 more
doaj   +1 more source

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, Johannes Haubner, Miroslav Kuchta, Geir Ringstad, Per Kristian Eide, Kent-Andre Mardal   +5 more
doaj   +1 more source

Determining COVID-19 Dynamics Using Physics Informed Neural Networks

Axioms, 2022
The Physics Informed Neural Networks framework is applied to the understanding of the dynamics of COVID-19. To provide the governing system of equations used by the framework, the Susceptible–Infected–Recovered–Death mathematical model is used.
Joseph Malinzi, Simanga Gwebu, Sandile Motsa   +2 more
doaj   +1 more source

General implementation of quantum physics-informed neural networks

Array, 2023
Recently, a novel type of Neural Network (NNs): the Physics-Informed Neural Networks (PINNs), was discovered to have many applications in computational physics.
Shashank Reddy Vadyala, Sai Nethra Betgeri   +1 more
doaj   +1 more source