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  2. Physics-informed neural networks - Wikipedia

    en.wikipedia.org/wiki/Physics-informed_neural...

    Physics-informed neural networks for solving Navier–Stokes equations. Physics-informed neural networks (PINNs), [1] also referred to as Theory-Trained Neural Networks (TTNs), [2] are a type of universal function approximators that can embed the knowledge of any physical laws that govern a given data-set in the learning process, and can be described by partial differential equations (PDEs).

  3. Machine learning in physics - Wikipedia

    en.wikipedia.org/wiki/Machine_learning_in_physics

    Physics informed neural networks have been used to solve partial differential equations in both forward and inverse problems in a data driven manner. [36] One example is the reconstructing fluid flow governed by the Navier-Stokes equations.

  4. Neural operators - Wikipedia

    en.wikipedia.org/wiki/Neural_operators

    Neural operators can be trained directly using backpropagation and gradient descent-based methods. Another training paradigm is associated with physics-informed machine learning. In particular, physics-informed neural networks (PINNs) use complete physics laws to fit neural

  5. Theory of functional connections - Wikipedia

    en.wikipedia.org/wiki/Theory_of_functional...

    Of particular note is the application of TFC in neural networks, where it has shown exceptional efficiency, [17] [18] [19] especially addressing high-dimensional problems and in enhancing the performance of physics-informed neural networks [20] by effectively eliminating constraints from the optimization process, a challenge that traditional ...

  6. Physical neural network - Wikipedia

    en.wikipedia.org/wiki/Physical_neural_network

    A physical neural network is a type of artificial neural network in which an electrically adjustable material is used to emulate the function of a neural synapse or a higher-order (dendritic) neuron model. [1] "Physical" neural network is used to emphasize the reliance on physical hardware used to emulate neurons as opposed to software-based ...

  7. Daniele Mortari - Wikipedia

    en.wikipedia.org/wiki/Daniele_Mortari

    Implementations of TFC in neural networks were first proposed by the Deep-TFC framework, then by the X-TFC using an Extreme learning machine, and by the Physics-informed neural networks (PINN). In particular, TFC allowed PINN to overcome the unbalanced gradients problem that often causes PINNs to struggle to accurately learn the underlying ...

  8. Types of artificial neural networks - Wikipedia

    en.wikipedia.org/wiki/Types_of_artificial_neural...

    Some artificial neural networks are adaptive systems and are used for example to model populations and environments, which constantly change. Neural networks can be hardware- (neurons are represented by physical components) or software-based (computer models), and can use a variety of topologies and learning algorithms.

  9. Jürgen Czarske - Wikipedia

    en.wikipedia.org/wiki/Jürgen_Czarske

    The areas of application of the ... Prof Czarske has achieved paradigm shifts in the combination of deep learning and physics with physics-informed neural networks ...