Jan 29, 2021 · The method brings together two main elements. First, ideas from projection-based model reduction are used to explicitly parametrize the learned ...

This work formulates a new approach to reduced modeling of parameterized, time-dependent partial differential equations (PDEs). The method employs Operator ...

Qian, A scientific machine learning approach to learning reduced models for nonlinear partial differential equations, PhD thesis, Massachusetts Institute of.

The method combines the perspectives of model reduction and machine learning. Model reduction brings in the physics of the problem, constraining the ROM ...

Reduced operator inference for nonlinear partial differential equations, in revision, 2021. Summary. The function inferOperators.m learns a reduced model for ...

The method brings together two main elements. First, ideas from projection-based model reduction are used to explicitly parametrize the learned model by low- ...

Operator Inference is a scientific machine learning technique for nonlinear model reduction and data-driven modeling.

Nov 1, 2023 · This review discusses Operator Inference, a nonintrusive reduced model- ing approach that incorporates physical governing equations by defining ...

We demonstrate this new capability for nonlinear model reduction in the PINNs framework by several nontrivial parametric partial differential equations. Non ...

The proposed approach generalizes to the PDE setting an Operator Inference method previously developed for systems of ordinary differential equations (ODEs) ...