Results 191 to 200 of about 8,116 (238)

Physics-informed neural network analysis of kerosene-based penta-hybrid nanofluid flow and heat transfer. [PDF]

Discov Nano
Makhdoum BM, Zahir H, Katbar NM, Jamshed W, Guedri K, Bayram M, Hussain SM, Batool S, Ahmad H.   +8 more
europepmc   +1 more source

Physics-informed neural network with adaptive loss balancing for real-time radiotherapy dose prediction and verification. [PDF]

Sci Rep
Zhang K, Li Y, Zhang J, Wang B, Chen Z.
europepmc   +1 more source

Learning variable-order time fractional diffusion equations using Physics-Informed Neural Networks. [PDF]

PLoS One
Ren L, Jin S.
europepmc   +1 more source

Physics-informed differentiable solvers for learning parametric solution manifolds in heterogeneous physical systems. [PDF]

PNAS Nexus
Panahi M, Porta GM, Riva M, Guadagnini A.   +3 more
europepmc   +1 more source

MoRPI-PINN: a physics-informed framework for mobile robot pure inertial navigation. [PDF]

Sci Rep
Sahoo AK, Klein I.
europepmc   +1 more source

Adaptive transfer learning for PINN

Journal of Computational Physics, 2023
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Yang Liu 0145, Wen Liu 0007, Xunshi Yan, Shuaiqi Guo, Chen-An Zhang   +4 more
core   +4 more sources

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Boundary-safe PINNs extension

Kalpa Publications in Computing, 2023
The goal of this work is to solve a nonlinear parabolic PDE problem that arise in the financial world by means of the so called PINNs methodology. We propose a novel treat- ment of the boundary conditions that allows us to avoid, as far as possible, the heuristic choice of the weights for the contributions of the boundary addends of the loss function ...
Joel P. Villarino, José Antonio García-Rodríguez, Álvaro Leitao   +2 more
openaire   +1 more source