MHA-PINN: A Novel Physics-Informed Neural Network for Predicting Fiber Dyeability. [PDF]
Zhou F +5 more
europepmc +1 more source
A PINN-driven game-theoretic framework in limited data photoacoustic tomography. [PDF]
Roy S, Pal S.
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Machine-learning-assisted photonic device development: a multiscale approach from theory to characterization. [PDF]
Chen Y +18 more
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Optical solutions to time-fractional improved (2+1)-dimensional nonlinear Schrödinger equation in optical fibers. [PDF]
Murad MAS, Tedjani AH, Li Z, Hussain E.
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Characterization of the spectra of rotating truncated gas planets and inertia-gravity modes. [PDF]
de Hoop MV, Holman S.
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A hybrid analytical-machine learning framework for data-driven modeling of soliton solutions in the complex Ginzburg-Landau equation. [PDF]
Muhammad J, Tedjani AH, Yao F, Younas U.
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Enhanced absorption in organic solar cells via combined anti-reflection and Mie resonance effects in subwavelength ellipsoidal dielectric nanostructures. [PDF]
Lim D +7 more
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Predictive framework to evaluate ternary nanocomposite over surface subjected to novel physical perspective. [PDF]
Bilal S, Asadullah.
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Ultrasound-assisted deep eutectic solvent of neuroprotective extracts from Paeonia lactiflora Pall. root: Process optimization, compositions characterization, and neuroprotective activity validation. [PDF]
Xue Y +8 more
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Learning stable radiation boundaries for wave simulations via passive neural state-space models. [PDF]
Li A, Wang X, Wang Y, Wang S.
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