M
Michael McCourt
Researcher at Intel
Publications - 61
Citations - 1272
Michael McCourt is an academic researcher from Intel. The author has contributed to research in topics: Bayesian optimization & Kernel (statistics). The author has an hindex of 14, co-authored 57 publications receiving 1009 citations. Previous affiliations of Michael McCourt include Cornell University & Argonne National Laboratory.
Papers
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Journal ArticleDOI
Multiphysics simulations: Challenges and opportunities
David E. Keyes,Lois Curfman McInnes,Carol S. Woodward,William Gropp,Eric Myra,Michael Pernice,John B. Bell,Jed Brown,Alain Clo,Jeffrey M. Connors,Emil M. Constantinescu,Donald Estep,Katherine J. Evans,Charbel Farhat,Ammar Hakim,Glenn E. Hammond,Glen A. Hansen,Judith Hill,Tobin Isaac,Xiangmin Jiao,Kirk E. Jordan,Dinesh K. Kaushik,Efthimios Kaxiras,Alice Koniges,Kihwan Lee,P. Aaron Lott,Qiming Lu,John H. Magerlein,Reed M. Maxwell,Michael McCourt,Miriam Mehl,Roger P. Pawlowski,Amanda Randles,Daniel R. Reynolds,Béatrice Rivière,Ulrich Rüde,Timothy D. Scheibe,John N. Shadid,Brendan Sheehan,Mark S. Shephard,Andrew R. Siegel,Barry Smith,Xian-Zhu Tang,Cian R. Wilson,Barbara Wohlmuth +44 more
TL;DR: This study considers multiphysics applications from algorithmic and architectural perspectives, where “algorithmic” includes both mathematical analysis and computational complexity, and “architectural’ includes both software and hardware environments.
Journal ArticleDOI
Stable Evaluation of Gaussian Radial Basis Function Interpolants
TL;DR: A new way to compute and evaluate Gaussian radial basis function interpolants in a stable way with a special focus on small values of the shape parameter, i.e., for “flat” kernels is provided.
Journal ArticleDOI
An introduction to the Hilbert-Schmidt SVD using iterated Brownian bridge kernels
TL;DR: A class of so-called iterated Brownian bridge kernels which allow the discussion to keep the discussion as simple and accessible as possible are introduced.
Posted Content
Bayesian Optimization for Machine Learning : A Practical Guidebook.
TL;DR: This guidebook outlines four example machine learning problems that can be solved using open source machine learning libraries, and highlights the benefits of using Bayesian optimization in the context of these common machine learning applications.