Zobrazeno 1 - 10
of 39
pro vyhledávání: '"Renganathan, S. Ashwin"'
We tackle the problem of quantifying failure probabilities for expensive computer experiments with stochastic inputs. The computational cost of evaluating the computer simulation prohibits direct Monte Carlo (MC) and necessitates a statistical surrog
Externí odkaz:
http://arxiv.org/abs/2410.04496
Autor:
Renganathan, S. Ashwin, Carlson, Kade
Emulating the mapping between quantities of interest and their control parameters using surrogate models finds widespread application in engineering design, including in numerical optimization and uncertainty quantification. Gaussian process models c
Externí odkaz:
http://arxiv.org/abs/2407.01495
Classical evolutionary approaches for multiobjective optimization are quite accurate but incur a lot of queries to the objectives; this can be prohibitive when objectives are expensive oracles. A sample-efficient approach to solving multiobjective op
Externí odkaz:
http://arxiv.org/abs/2310.15788
Bayesian deep Gaussian processes (DGPs) outperform ordinary GPs as surrogate models of complex computer experiments when response surface dynamics are non-stationary, which is especially prevalent in aerospace simulations. Yet DGP surrogates have not
Externí odkaz:
http://arxiv.org/abs/2308.04420
Estimating probability of failure in aerospace systems is a critical requirement for flight certification and qualification. Failure probability estimation involves resolving tails of probability distribution, and Monte Carlo sampling methods are int
Externí odkaz:
http://arxiv.org/abs/2203.01436
Wind farm design primarily depends on the variability of the wind turbine wake flows to the atmospheric wind conditions, and the interaction between wakes. Physics-based models that capture the wake flow-field with high-fidelity are computationally v
Externí odkaz:
http://arxiv.org/abs/2109.02411
Wind turbine wakes are the result of the extraction of kinetic energy from the incoming atmospheric wind exerted from a wind turbine rotor. Therefore, the reduced mean velocity and enhanced turbulence intensity within the wake are affected by the cha
Externí odkaz:
http://arxiv.org/abs/2109.01646
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate stochastic oracle. We are interested in the decision that maximizes the oracle at a finite time horizon, given a limited budget of noisy evaluation
Externí odkaz:
http://arxiv.org/abs/2105.09824
Adjoint-based optimization methods are attractive for aerodynamic shape design primarily due to their computational costs being independent of the dimensionality of the input space and their ability to generate high-fidelity gradients that can then b
Externí odkaz:
http://arxiv.org/abs/2008.06731
We propose a novel Bayesian method to solve the maximization of a time-dependent expensive-to-evaluate oracle. We are interested in the decision that maximizes the oracle at a finite time horizon, when relatively few noisy evaluations can be performe
Externí odkaz:
http://arxiv.org/abs/2006.08037