Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets

@article{Perdikaris2016MultifidelityIF,
  title={Multifidelity Information Fusion Algorithms for High-Dimensional Systems and Massive Data sets},
  author={Paris Perdikaris and Daniele Venturi and George Em Karniadakis},
  journal={SIAM J. Scientific Computing},
  year={2016},
  volume={38}
}
We develop a framework for multifidelity information fusion and predictive inference in high-dimensional input spaces and in the presence of massive data sets. Hence, we tackle simultaneously the “big N” problem for big data and the curse of dimensionality in multivariate parametric problems. The proposed methodology establishes a new paradigm for constructing response surfaces of high-dimensional stochastic dynamical systems, simultaneously accounting for multifidelity in physical models as… CONTINUE READING
10 Citations
34 References
Similar Papers

Citations

Publications citing this paper.
Showing 1-10 of 10 extracted citations

References

Publications referenced by this paper.
Showing 1-10 of 34 references

Virtual Library of Simulation Experiments: Test Functions and Datasets

  • S. Surjanovic, D. Bingham
  • http://www.sfu.ca/∼ssurjano
  • 2015
Highly Influential
3 Excerpts

Efficient input-output model representations

  • H. Rabitz, Ö.F. Aliş, J. Shorter, K. Shim
  • Comput. Phys. Commun., 117
  • 1999
Highly Influential
10 Excerpts

Similar Papers

Loading similar papers…