RESEARCH

Our research spans numerous topics ranging from applied mathematics to Bayesian inference, and computational simulation techniques. Do check out some of our research outputs below.

PAPERS

Prior-informed uncertainty modelling with Bayesian polynomial approximations
Chun Yui Wong, Pranay Seshadri, Andrew Duncan, Ashley Scillitoe, Geoff Parks
Under review (2022)
preprint | discourse
Bayesian inference orthogonal polynomials

Polynomial ridge flow field estimation
Ashley Scillitoe, Pranay Seshadri, Chun Yui Wong, Andrew B. Duncan
Physics of Fluids (2021)
preprint
active subspaces ridge approximations embedded ridges orthogonal polynomials

Extremum sensitivity analysis with polynomial Monte Carlo filtering
Chun Yui Wong, Pranay Seshadri, Geoff Parks
Reliability Engineering & System Safety (2021)
preprint | paper | discourse
active subspaces ridge approximations sensitivity analysis orthogonal polynomials

Embedded ridge approximations
Chun Yui Wong, Pranay Seshadri, Geoff Parks, Mark Girolami
Computer Methods in Applied Mechanics & Engineering (2020)
preprint | paper | discourse
active subspaces ridge approximations embedded ridges orthogonal polynomials

Blade envelopes part I: concept & methodology
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Andrew B. Duncan, Geoff Parks
ASME Journal of Turbomachinery (2022)
preprint | discourse
active subspaces ridge approximations embedded ridges orthogonal polynomials distance metrics

Blade envelopes part II: multiple objectives & inverse design
Chun Yui Wong, Pranay Seshadri, Ashley Scillitoe, Bryn Noel Ubald, Andrew B. Duncan, Geoff Parks
ASME Journal of Turbomachinery (2022)
preprint | discourse
active subspaces ridge approximations embedded ridges orthogonal polynomials distance metrics

Effectively subsampled quadratures for least squares polynomial approximations
Pranay Seshadri, Akil Narayan, Sankaran Mahadevan
SIAM/ASA Journal on Uncertainty Quantification (2017)
preprint | paper
orthogonal polynomials numerical integration