Generative discovery of partial differential equations by learning from math handbooks. [PDF]
Nat CommunXu H +7 more
europepmc +1 more source
Deterministic, stochastic, and mean-field PDE models in neuroscience. [PDF]
Front Comput NeurosciÇetin C +5 more
europepmc +1 more source
Gradient-Driven Physics Informed Neural Networks for Conduction Heat Transfer and Incompressible Laminar Flow. [PDF]
J Comput Nonlinear DynLu T +5 more
europepmc +1 more source
A fast, accurate and oscillation-free spectral collocation solver for high-dimensional transport problems. [PDF]
Sci RepCavallini N +3 more
europepmc +1 more source
NeuberNet: a neural operator solving elastic-plastic partial differential equations at V-notches from low-fidelity elastic simulations. [PDF]
Commun EngGrossi T, Beghini M, Benedetti M.
europepmc +1 more source
Physics-informed neural networks for physiological signal processing and modeling: a narrative review. [PDF]
Physiol MeasZhao A, Fattahi D, Hu X.
europepmc +1 more source
Active control of flexible spacecraft in orbit based on partial differential equations. [PDF]
PLoS OneZhang B, Wen M.
europepmc +1 more source
Supervised machine learning computing paradigm of energy activation for magnetic nanofluid flow via porous surface with nonlinear variant viscosity. [PDF]
Discov NanoAlhubieshi N +7 more
europepmc +1 more source
The Hard-Constraint PINNs for Interface Optimal Control Problems
We show that the physics-informed neural networks (PINNs), in combination with some recently developed discontinuity capturing neural networks, can be applied to solve optimal control problems subject to partial differential equations (PDEs) with ...
Lai, Ming-Chih +4 more
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