Hybridndiff-UQ: Uncertainty quantification for hybrid neural differentiable modeling. [PDF]
Theor Appl Mech LettAkhare D, Luo T, Wang JX.
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Information-distilled physics informed deep learning for high order differential inverse problems with extreme discontinuities. [PDF]
Commun EngPeng M, Tang H.
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A PINN-driven game-theoretic framework in limited data photoacoustic tomography. [PDF]
Inverse ProblRoy S, Pal S.
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WF-PINNs: solving forward and inverse problems of burgers equation with steep gradients using weak-form physics-informed neural networks. [PDF]
Sci RepWang X, Yi S, Gu H, Xu J, Xu W.
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Material Data Identification in an Induction Hardening Test Rig with Physics-Informed Neural Networks. [PDF]
Materials (Basel), 2023 Asadzadeh MZ, Roppert K, Raninger P.
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A locally adaptive regularization of a hybrid variational model for color image diffusion via integration of diffusion with normalized data. [PDF]
Sci RepArain MB +6 more
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Physics-informed neural networks for physiological signal processing and modeling: a narrative review. [PDF]
Physiol MeasZhao A, Fattahi D, Hu X.
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Physics-Informed Deep-Learning For Elasticity: Forward, Inverse, and Mixed Problems. [PDF]
Adv Sci (Weinh), 2023 Chen CT, Gu GX.
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Nonlocal Attention Operator: Materializing Hidden Knowledge Towards Interpretable Physics Discovery. [PDF]
Adv Neural Inf Process SystYu Y +5 more
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Physics-informed neural network reconciles Australian displacements and tectonic stresses. [PDF]
Sci Rep, 2023 Poulet T, Behnoudfar P.
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