Reduced Basis PMOR method

The Reduced Basis Method (RBM) we present here is applicable to static and time-dependent linear PDEs.

Time-Independent PDEs

The typical model problem of the RBM consists of a parametrized PDE stated in weak form with bilinear form $a(\cdot ,\cdot ;\mu )$ and linear form $f(\cdot ;\mu )$. The parameter $\mu$ is considered within a domain ${𝒟}$ and we are interested in an output quantity $s(\mu )$ which can be expressed via a linear functional of the field variable $l(\cdot ;\mu )$.

The exact, infinite-dimensional formulation, indicated by the superscript e, is given by

$\{\begin{array}{l}\text{For }\mu \in {𝒟}\subset {\mathbb{ℝ}}^P,\text{ evaluate }\\ s^e(\mu )=l(u^e(\mu );\mu ),\\ \text{where }{u}^e(\mu )\in X^e(\mathrm{\Omega })\text{ satisfies }\\ a(u^e(\mu ),v;\mu )=f(v;\mu ),\mathrm{\forall }v\in X^e.\end{array}$

We assume a large-scale discretization to be given, such that we consider

$\{\begin{array}{l}\text{For }\mu \in {𝒟}\subset {\mathbb{ℝ}}^P,\text{ evaluate }\\ s(\mu )=l(u(\mu );\mu ),\\ \text{where }u(\mu )\in X(\mathrm{\Omega })\text{ satisfies }\\ a(u(\mu ),v;\mu )=f(v;\mu ),\mathrm{\forall }v\in X.\end{array}$

The underlying assumption of the RBM is that the parametrically induced manifold ${ℳ}=\{u(\mu )|\mu \in {𝒟}\}$ can be approximated by a low dimensional space $V_N$.

It also applies the concept of an offline-online decomposition, in that a large pre-processing offline cost is acceptable in view of a very low online cost (of a reduced order model) for each input-output evaluation, when in a many-query or real-time context.

The essential assumption which allows the offline-online decomposition is that there exists an affine parameter dependence

$a(w,v;\mu )=\sum _{q=1}^Q{\mathrm{\Theta }}_a^q(\mu )a^q(w,v)$

$f(v;\mu )=\sum _{q=1}^{Q^f}{\mathrm{\Theta }}_f^q(\mu )f^q(v).$

The Lagrange Reduced Basis space is established by iteratively choosing Lagrange parameter samples

$S_N=\{{\mu }^1,...,{\mu }^N\}$

and considering the associated Lagrange RB spaces

$V_N=\text{span}\{u^{{𝒩}}({\mu }^n),1\le n\le N\}$

in a greedy sampling. This leads to hierarchical RB spaces: $V_1\subset V_2\subset ...\subset V_{N_{max}}$.

We then consider the galerkin projection onto the RB-space $V_N$

$\{\begin{array}{l}\text{For }\mu \in {𝒟}\subset {\mathbb{ℝ}}^P,\text{ evaluate }\\ s_N(\mu )=f(u_N(\mu )),\\ \text{where }{u}_N(\mu )\in W_N\text{ satisfies }\\ a(u_N(\mu ),v;\mu )=f(v),\mathrm{\forall }v\in W_N\end{array}$

The greedy sampling uses an error estimator ${\mathrm{\Delta }}_N(\mu )$ which estimates (even rigorously, in some cases) the approximation error $/|u(\mu )-u_N(\mu )/|$.

Let $\mathrm{\Xi }$ denote a finite sample of ${𝒟}$ and set $S_1=\{{\mu }^1\}\mathrm{\$}and\mathrm{\$}{V}_1=span\{u({\mu }^1)\}$. For Failed to parse (syntax error): {\displaystyle N = 2 , ... , N_{max} <math>, find <math> \mu^N = \text{arg max}_{\substack{ \mu \in \Xi }} \Delta_{N-1}(\mu) } , and then set $S_N=S_{N-1}\cup {\mu }^N,V_N=V_{N-1}+span\{u({\mu }^N)\}$.