no code implementations • 5 May 2023 • Ahmed Attia, Sven Leyffer, Todd Munson
This work presents an efficient algorithmic approach for designing optimal experimental design schemes for Bayesian inverse problems such that the optimal design is robust to misspecification of elements of the inverse problem.
no code implementations • 20 Nov 2021 • Dominic Yang, Prasanna Balaprakash, Sven Leyffer
We consider nonlinear optimization problems that involve surrogate models represented by neural networks.
no code implementations • 16 Feb 2021 • Jongeun Kim, Sven Leyffer, Prasanna Balaprakash
This problem is referred to as symbolic regression.
no code implementations • 15 Jan 2021 • Ahmed Attia, Sven Leyffer, Todd Munson
We present a novel stochastic approach to binary optimization for optimal experimental design (OED) for Bayesian inverse problems governed by mathematical models such as partial differential equations.
1 code implementation • 20 Sep 2020 • Selin Aslan, Zhengchun Liu, Viktor Nikitin, Tekin Bicer, Sven Leyffer, Doga Gursoy
In our simulations, we demonstrate that our proposed framework with parameter tuning and learned priors generates high-quality reconstructions under limited and noisy measurement data.