| Finite element method-enhanced neural network for forward and inverse problems RE Meethal, A Kodakkal, M Khalil, A Ghantasala, B Obst, KU Bletzinger, ... Advanced Modeling and Simulation in Engineering Sciences 10 (1), 6, 2023 | 18 | 2023 |
| Uncertainties in dynamic response of buildings with non-linear base-isolators A Kodakkal, SK Saha, K Sepahvand, VA Matsagar, F Duddeck, S Marburg Engineering Structures 197, 109423, 2019 | 14 | 2019 |
| Risk-averse design of tall buildings for uncertain wind conditions A Kodakkal, B Keith, U Khristenko, A Apostolatos, KU Bletzinger, ... Computer Methods in Applied Mechanics and Engineering 402, 115371, 2022 | 8 | 2022 |
| Machine learning driven damper for response control in vehicle–bridge interaction systems K Rajnish, A Kodakkal, DH Zelleke, RE Meethal, VA Matsagar, ... Proceedings of the Institution of Civil Engineers-Bridge Engineering, 1-23, 2023 | 5 | 2023 |
| Stochastic response of primary–secondary coupled systems under uncertain ground excitation using generalized polynomial chaos method A Kodakkal, P Jagtap, V Matsagar Handbook of Probabilistic Models, 383-435, 2020 | 4 | 2020 |
| A Finite Element Method-Informed Neural Network For Uncertainty Quantification A Kodakkal, RE Meethal, B Obst, R WUCHNER 14th WCCM-ECCOMAS Congress 2020 800, 2021 | 2 | 2021 |
| D6. 4 Report on stochastic optimisation for unsteady problems Q Ayoul-Guilmard, F Nobile, S Ganesh, M Nuñez, A Kodakkal, R Rossi, ... Open Access Repository of the ExaQUte project: Deliverables 9, 2021 | 2 | 2021 |
| Multi-fidelity uncertainty quantification of high Reynolds number turbulent flow around a rectangular 5: 1 cylinder M Sakuma, N Pepper, S Warnakulasuriya, F Montomoli, R Wuch-ner, ... Wind and Structures 34 (1), 127-136, 2022 | 1 | 2022 |
| Finite Element Method-enhanced Neural Network for Forward and Inverse Problems R Ellath Meethal, B Obst, M Khalil, A Ghantasala, A Kodakkal, ... arXiv e-prints, arXiv: 2205.08321, 2022 | | 2022 |
| D6. 5 Report on stochastic optimisation for wind engineering F Nobile, Q Ayoul-Guilmard, S Ganesh, M Nuñez, A Kodakkal, C Soriano, ... | | 2022 |
| D7. 4 Final report on Stochastic Optimization results S Bidier, U Khristenko, A Kodakkal, C Soriano, R Rossi | | 2022 |
| ExaQUte: D6. 4 Report on stochastic optimisation for unsteady problems Q Ayoul-Guilmard, F Nobile, S Ganesh, M Núñez Corbacho, A Kodakkal, ... | | 2021 |
| D1. 4 Final public Release of the solver F Nobile, RM Badia, J Ejarque, L Cirrottola, A Froehly, B Keith, ... | | 2021 |
| ExaQUte: D1. 4 Final public release of the solver Q Ayoul-Guilmard, S Ganesh, F Nobile, RM Badia Sala, J Ejarque, ... | | 2020 |
| D2. 3. Adjoint-based error estimation routines B Keith, A Apostolatos, A Kodakkal, R Rossi, R Tosi, B Wohlmuth, ... | | 2020 |
| ExaQUte: D2. 3. Adjoint-based error estimation routines B Keith, A Apostolatos, A Kodakkal, R Rossi, R Tosi, B Wohlmuth | | 2019 |
| Multilevel Monte Carlo Method for Stochastic Analysis of Fluid‐Structure Interaction A Kodakkal, R Wüchner, KU Bletzinger PAMM 18 (1), e201800148, 2018 | | 2018 |
| Two Step Uncertainty Quantification Using Gradient Enhanced Stochastic Collocation for Geometric Uncertainties in FSI Problems. A Kodakkal, A Ghantasala, M Andre, R Wüchner, KU Bletzinger Frontiers of Uncertainty Quantification in Engineering, 2017, 2017 | | 2017 |
Roland WüchnerFull Professor and Head of Institute of Structural Analysis at TU BraunschweigVerified email at tu-braunschweig.de
