Michael Donello
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View article: Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with time-dependent bases
Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with time-dependent bases Open
Time-dependent basis reduced-order models (TDB ROMs) have successfully been used for approximating the solution to nonlinear stochastic partial differential equations (PDEs). For many practical problems of interest, discretizing these PDEs…
View article: Oblique projection for scalable rank-adaptive reduced-order modeling of nonlinear stochastic PDEs with time-dependent bases
Oblique projection for scalable rank-adaptive reduced-order modeling of nonlinear stochastic PDEs with time-dependent bases Open
Time-dependent basis reduced order models (TDB ROMs) have successfully been used for approximating the solution to nonlinear stochastic partial differential equations (PDEs). For many practical problems of interest, discretizing these PDEs…
View article: Constant Diffusion Burgers Code from Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with
Constant Diffusion Burgers Code from Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with Open
MATLAB code to solve the stochastic Burgers equation using TDB-CUR with oversampling
View article: Constant Diffusion Burgers Code from Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with
Constant Diffusion Burgers Code from Oblique projection for scalable rank-adaptive reduced-order modelling of nonlinear stochastic partial differential equations with Open
MATLAB code to solve the stochastic Burgers equation using TDB-CUR with oversampling
View article: Computing Sensitivities in Evolutionary Systems: A Real-Time Reduced Order Modeling Strategy
Computing Sensitivities in Evolutionary Systems: A Real-Time Reduced Order Modeling Strategy Open
We present a new methodology for computing sensitivities in evolutionary systems using a model-driven low-rank approximation. To this end, we formulate a variational principle that seeks to minimize the distance between the time derivative…