Sergey Oladyshkin

According to our database, Sergey Oladyshkin authored at least 18 papers between 2012 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Surrogate-assisted global sensitivity analysis of a hybrid-dimensional Stokes-Brinkman-Darcy model.

Linheng Ruan, Ilja Kröker, Sergey Oladyshkin, Iryna Rybak

CoRR, February, 2026

2025

CODE: A global approach to ODE dynamics learning.

Nils Wildt, Daniel M. Tartakovsky, Sergey Oladyshkin, Wolfgang Nowak

CoRR, November, 2025

2024

Inferring Underwater Topography with FINN.

Cosku Can Horuz, Matthias Karlbauer, Timothy Praditia, Sergey Oladyshkin, Wolfgang Nowak, Sebastian Otte

CoRR, 2024

2023

Physical Domain Reconstruction with Finite Volume Neural Networks.

Cosku Can Horuz, Matthias Karlbauer, Timothy Praditia, Martin V. Butz, Sergey Oladyshkin, Wolfgang Nowak, Sebastian Otte

Appl. Artif. Intell., December, 2023

The deep arbitrary polynomial chaos neural network or how Deep Artificial Neural Networks could benefit from data-driven homogeneous chaos theory.

Sergey Oladyshkin, Timothy Praditia, Ilja Kröker, Farid Mohammadi, Wolfgang Nowak, Sebastian Otte

Neural Networks, September, 2023

A fully Bayesian sparse polynomial chaos expansion approach with joint priors on the coefficients and global selection of terms.

Paul-Christian Bürkner, Ilja Kröker, Sergey Oladyshkin, Wolfgang Nowak

J. Comput. Phys., September, 2023

2022

Replication Data for: Learning Groundwater Contaminant Diffusion-Sorption Processes with a Finite Volume Neural Network.

Timothy Praditia, Matthias Karlbauer, Sebastian Otte, Sergey Oladyshkin, Martin V. Butz, Wolfgang Nowak

Dataset, November, 2022

Finite Volume Neural Network.

Timothy Praditia, Matthias Karlbauer, Sebastian Otte, Sergey Oladyshkin, Martin V. Butz, Wolfgang Nowak

Dataset, October, 2022

Arbitrary multi-resolution multi-wavelet-based polynomial chaos expansion for data-driven uncertainty quantification.

Ilja Kröker, Sergey Oladyshkin

Reliab. Eng. Syst. Saf., 2022

Composing Partial Differential Equations with Physics-Aware Neural Networks.

Matthias Karlbauer, Timothy Praditia, Sebastian Otte, Sergey Oladyshkin, Wolfgang Nowak, Martin V. Butz

Proceedings of the International Conference on Machine Learning, 2022

Infering Boundary Conditions in Finite Volume Neural Networks.

Cosku Can Horuz, Matthias Karlbauer, Timothy Praditia, Martin V. Butz, Sergey Oladyshkin, Wolfgang Nowak, Sebastian Otte

Proceedings of the Artificial Neural Networks and Machine Learning - ICANN 2022, 2022

2021

Finite Volume Neural Network: Modeling Subsurface Contaminant Transport.

Timothy Praditia, Matthias Karlbauer, Sebastian Otte, Sergey Oladyshkin, Martin V. Butz, Wolfgang Nowak

CoRR, 2021

2020

Reliability analysis with stratified importance sampling based on adaptive Kriging.

Sinan Xiao, Sergey Oladyshkin, Wolfgang Nowak

Reliab. Eng. Syst. Saf., 2020

Bayesian3 Active Learning for the Gaussian Process Emulator Using Information Theory.

Sergey Oladyshkin, Farid Mohammadi, Ilja Kröker, Wolfgang Nowak

Entropy, 2020

2019

The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design.

Sergey Oladyshkin, Wolfgang Nowak

Entropy, 2019

2018

Incomplete statistical information limits the utility of high-order polynomial chaos expansions.

Sergey Oladyshkin, Wolfgang Nowak

Reliab. Eng. Syst. Saf., 2018

Comparison of data-driven uncertainty quantification methods for a carbon dioxide storage benchmark scenario.

Markus Köppel, Fabian Franzelin, Ilja Kröker, Sergey Oladyshkin, Gabriele Santin, Dominik Wittwar, Andrea Barth, Bernard Haasdonk, Wolfgang Nowak, Dirk Pflüger, Christian Rohde

CoRR, 2018

2012

Data-driven uncertainty quantification using the arbitrary polynomial chaos expansion.

Sergey Oladyshkin, Wolfgang Nowak

Reliab. Eng. Syst. Saf., 2012