Results 111 to 120 of about 13,405 (259)

Fourier Domain Physics Informed Neural Network

Ultrafast optics is driven by a myriad of complex nonlinear dynamics. The ubiquitous presence of governing equations in the form of partial integro-differential equations (PIDE) necessitates the need for advanced computational tools to understand the underlying physical mechanisms.
Musgrave, Jonathan, Huang, Shu-Wei
openaire   +2 more sources

Tissue Engineered Human Elastic Cartilage From Primary Auricular Chondrocytes for Ear Reconstruction

Advanced Functional Materials, EarlyView.
Despite over three decades of research, no tissue‐engineered solution for auricular reconstruction in microtia patients has reached clinical translation. The key challenge lies in generating functional elastic cartilage ex vivo. Here, we integrate synergistic cell‐biomaterial strategies to engineer auricular grafts with mechanical and histological ...
Philipp Fisch, Sandra Kessler, Simone Ponta, Anna Puiggalí‐Jou, Guoliang Lyu, Killian Flégeau, Anastasiya Martyts, Florian Roth, David Fercher, Filippo M. Rijli, Daniel Simmen, Eva Novoa Olivares, Thomas Linder, Marcy Zenobi‐Wong   +13 more
wiley   +1 more source

FastLRNR and Sparse Physics Informed Backpropagation

Results in Applied Mathematics
We introduce Sparse Physics Informed Backpropagation (SPInProp), a new class of methods for accelerating backpropagation for a specialized neural network architecture called Low Rank Neural Representation (LRNR).
Woojin Cho, Kookjin Lee, Noseong Park, Donsub Rim, Gerrit Welper   +4 more
doaj   +1 more source

Constrained Hamiltonian systems and physics-informed neural networks: Hamilton-Dirac neural networks

Physical Review E
The effectiveness of the Physics Informed Neural Networks (PINNs) for learning the dynamics of constrained Hamiltonian systems is demonstrated using the Dirac theory of constraints for regular systems with holonomic constraints and systems with non-standard Lagrangians.
openaire   +3 more sources

Physics-Informed Neural Networks and Extensions

Frontiers of Science Awards ...
Raissi, Maziar, Perdikaris, Paris, Ahmadi, Nazanin, Karniadakis, George Em   +3 more
openaire   +2 more sources

Loss-attentional physics-informed neural networks

Journal of Computational Physics
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Song, Y., Wang, H., Yang, H., Taccari, M.L., Chen, X.   +4 more
openaire   +3 more sources

The Cuttlebone Blueprint for Multifunctional Metamaterials: Design Taxonomy, Functional Decoupling, and Future Horizons

Advanced Functional Materials, EarlyView.
Cuttlebone‐inspired metamaterials exploit a septum‐wall architecture to achieve excellent mechanical and functional properties. This review classifies existing designs into direct biomimetic, honeycomb‐type, and strut‐type architectures, summarizes governing design principles, and presents a decoupled design framework for interpreting multiphysical ...
Xinwei Li, Zhendong Li
wiley   +1 more source

Optoelectronic Synaptic Devices Using Molecular Telluride Phase‐Change Inks for Three‐Factor Learning

Advanced Functional Materials, EarlyView.
Optoelectronic synaptic devices based on solution‐processed molecular telluride GST‐225 phase‐change inks are demonstrated for three‐factor learning. A global optical signal broadcast through a silicon waveguide induces non‐volatile conductance updates exclusively in locally electrically flagged memristors.
Kevin Portner, Dhananjeya Kumaar, Giacomo Torti, Killian Keller, Till Zellweger, Florian M. Schenk, Christoph Weilenmann, Daniel Chelladurai, Vara Prasad Jonnalagadda, Ghazi Sarwat Syed, Vanessa Wood, Jürg Leuthold, Alexandros Emboras, Maksym Yarema, Mathieu Luisier   +14 more
wiley   +1 more source

Constrained optimization in physics-informed neural networks for singular three-point boundary value problems

Ain Shams Engineering Journal
Physics-informed neural networks represent a category of deep learning models that directly incorporate physical laws into the training process to solve differential equations, thereby diminishing the dependence on extensively labeled datasets.
Uzma Nadeem, Malik Ahsin Iqbal, Sadique Rehman, Aamir Farooq, Hijaz Ahmad, Fuad A. Awwad, Emad A.A. Ismail   +6 more
doaj   +1 more source

From Bug to Feature: Harnessing Cross‐Sensitivity for Multiparametric Luminescence Sensing

Advanced Functional Materials, EarlyView.
Cross‐sensitivity in luminescence sensing is reframed from a limitation into a resource for multiparametric detection. Using ruby microspheres as a model system, cross‐sensitivity is quantitatively assessed and exploited through linear discriminant analysis, enabling simultaneous, correction‐free pressure and temperature sensing with a single ...
Nikita Panov, Liyan Ming, Stanislav Sinogeikin, Erving Ximendes, Daniel Jaque, Riccardo Marin   +5 more
wiley   +1 more source