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{{Short description|Machine learning framework}}
'''Neural operators''' are a class of [[deep learning]] architectures designed to learn maps between infinite-dimensional [[
The primary application of neural operators is in learning surrogate maps for the solution operators of [[
== Operator learning ==
Understanding and mapping relationships between function spaces has many applications in engineering and the sciences. In particular, [[Abstract differential equation|one can cast the problem]] of solving partial differential equations as identifying a map between function spaces, such as from an initial condition to a time-evolved state. In other PDEs this map takes an input coefficient function and outputs a solution function. Operator learning is a [[machine learning]] paradigm to learn solution operators mapping the input function to the output function.
Using traditional machine learning methods, addressing this problem would involve discretizing the infinite-dimensional input and output function spaces into finite-dimensional grids and applying standard learning models, such as neural networks. This approach reduces the operator learning to finite-dimensional function learning and has some limitations, such as generalizing to discretizations beyond the grid used in training.
The primary properties of neural operators that differentiate them from traditional neural networks is discretization invariance and discretization convergence.<ref name="NO journal" />
== Definition and formulation ==
Architecturally, neural operators are similar to feed-forward neural networks in the sense that they are
Neural operators seek to approximate some operator <math>\mathcal{G} : \mathcal{A} \to \mathcal{U}</math> between function spaces <math>\mathcal{A}</math> and <math>\mathcal{U}</math> by building a parametric map <math>\mathcal{G}_\phi : \mathcal{A} \to \mathcal{U}</math>. Such parametric maps <math>\mathcal{G}_\phi</math> can generally be defined in the form
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<math>\mathcal{G}_\phi := \mathcal{Q} \circ \sigma(W_T + \mathcal{K}_T + b_T) \circ \cdots \circ \sigma(W_1 + \mathcal{K}_1 + b_1) \circ \mathcal{P},</math>
where <math>\mathcal{P}, \mathcal{Q}</math> are the lifting (lifting the codomain of the input function to a higher dimensional space) and projection (projecting the codomain of the intermediate function to the output
<math>(\mathcal{K}_\phi v_t)(x) := \int_D \kappa_\phi(x, y, v_t(x), v_t(y))v_t(y)dy, </math>
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<math>v_{t+1}(x) \approx \sigma\left(\sum_j^n \kappa_\phi(x, y_j, v_t(x), v_t(y_j))v_t(y_j)\Delta_{y_j} + W_t(v_t(y_j)) + b_t(x)\right).</math>
The above approximation, along with parametrizing <math>\kappa_\phi</math> as an implicit neural network, results in the graph neural operator (GNO).<ref name="Graph NO">{{cite arXiv |last1=Li |first1=Zongyi |last2=Kovachki |first2=Nikola |last3=Azizzadenesheli |first3=Kamyar |last4=Liu |first4=Burigede |last5=Bhattacharya |first5=Kaushik |last6=Stuart |first6=Andrew |last7=Anima |first7=Anandkumar |title=Neural operator: Graph kernel network for partial differential equations |date=2020 |class=cs.LG |eprint=2003.03485 }}</ref>
There have been various parameterizations of neural operators for different applications.<ref name="FNO" /><ref name="Graph NO" />
<math>(\mathcal{K}_\phi v_t)(x) = \mathcal{F}^{-1} (R_\phi \cdot (\mathcal{F}v_t))(x), </math>
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where <math>\|\cdot \|_\mathcal{U}</math> is a norm on the output function space <math>\mathcal{U}</math>. 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 networks to solutions of PDEs. Extensions of this paradigm to operator learning are broadly called physics-informed neural operators (PINO),<ref name="PINO">{{cite arXiv |last1=Li |first1=Zongyi | last2=Hongkai| first2=Zheng |last3=Kovachki |first3=Nikola | last4=Jin | first4=David | last5=Chen | first5= Haoxuan |last6=Liu |first6=Burigede | last7=Azizzadenesheli |first7=Kamyar |last8=Anima |first8=Anandkumar |title=Physics-Informed Neural Operator for Learning Partial Differential Equations |date=2021 |class=cs.LG |eprint=2111.03794 }}</ref>
== See also ==
* [[Neural network (machine learning)|Neural network]]
* [[Physics-informed neural networks]]
* [[Neural field]]
== References ==
{{reflist}}
== External links ==
*[https://github.com/neuraloperator/neuraloperator/ neuralop] – Python library of various neural operator architectures
[[Category:Deep learning]]
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