<oai_dc:dc xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd">
  <dc:creator>Ben Bader, Seif</dc:creator>
  <dc:creator>Benedusi, Pietro</dc:creator>
  <dc:creator>Quaglino, Alessio</dc:creator>
  <dc:creator>Zulian, Patrick</dc:creator>
  <dc:creator>Krause, Rolf</dc:creator>
  <dc:date>2021-02-05</dc:date>
  <dc:description xmlns:ns0="xml" ns0:lang="en">We present a novel approach aimed at high-performance uncertainty quantification for time-dependent problems governed by partial  differential equations. In particular, we consider input uncertainties described by a Karhunen-Loève expansion and compute statistics of  high-dimensional quantities-of-interest, such as the cardiac activation potential. Our methodology relies on a close integration of  multilevel Monte Carlo methods, parallel iterative solvers, and a space-time discretization. This combination allows for space-time  adaptivity, time-changing domains, and to take advantage of past samples to initialize the space-time solution. The resulting sequence  of problems is distributed using a multilevel parallelization strategy, allocating batches of samples having different sizes to a different  number of processors. We assess the performance of the proposed framework by showing in detail its application to the solution of  nonlinear equations arising from cardiac electrophysiology. Specifically, we study the effect of spatially-correlated perturbations of the  heart fibers' conductivities on the mean and variance of the resulting activation map. As shown by the experiments, the theoretical rates  of convergence of multilevel Monte Carlo are achieved. Moreover, the total computational work for a prescribed accuracy is reduced by  an order of magnitude with respect to standard Monte Carlo methods.</dc:description>
  <dc:format>application/pdf</dc:format>
  <dc:identifier>https://n2t.net/ark:/12658/srd1319324</dc:identifier>
  <dc:identifier>https://susi.usi.ch/global/documents/319324</dc:identifier>
  <dc:identifier>https://susi.usi.ch/documents/319324/files/BenBader_2021_Else_jcp.pdf</dc:identifier>
  <dc:language>eng</dc:language>
  <dc:relation>info:eu-repo/semantics/altIdentifier/doi/10.1016/j.jcp.2021.110164</dc:relation>
  <dc:relation>info:eu-repo/semantics/altIdentifier/ark/12658/srd1319324</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>CC BY-NC-ND</dc:rights>
  <dc:source>Journal of computational physics. - 2021, vol. 433, no. May, p. 110164</dc:source>
  <dc:subject xmlns:ns1="xml" ns1:lang="en">Uncertainty quantification</dc:subject>
  <dc:subject xmlns:ns2="xml" ns2:lang="en">Multilevel methods</dc:subject>
  <dc:subject xmlns:ns3="xml" ns3:lang="en">Space-time finite elements (3D+1)</dc:subject>
  <dc:subject xmlns:ns4="xml" ns4:lang="en">Cardiac electrophysiology</dc:subject>
  <dc:subject xmlns:ns5="xml" ns5:lang="en">Monodomain equation</dc:subject>
  <dc:subject>info:eu-repo/classification/udc/004</dc:subject>
  <dc:title xmlns:ns6="xml" ns6:lang="en">Space-time multilevel Monte Carlo methods and their application to cardiac electrophysiology</dc:title>
  <dc:type>http://purl.org/coar/resource_type/c_6501</dc:type>
</oai_dc:dc>
