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Schindler, Felix Tobias

Publications

26 entries

per page

Pageof3

entries 1-10 of 26Pageof3

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling Wenzel, Tizian; Haasdonk, Bernard; Kleikamp, Hendrik; Ohlberger, Mario; Schindler, Felix (2024) In: Lirkov, I.; Margenov, S. (eds.), Large-Scale Scientific Computations, 117-125. Cham: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
Adaptive Localized Reduced Basis Methods for Large Scale PDE-constrained Optimization Keil, Tim; Ohlberger, Mario; Schindler, Felix (2024) In: Lirkov, I; Margenov, S (eds.), Large-Scale Scientific Computations, 108-116. Cham: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
Local training and enrichment based on a residual localization strategy Tim Keil, Mario Ohlberger, Felix Schindler, Julia Schleuß (2024) In: Frolkovič, P; Mikula, K; Ševčovič, D (eds.), Proceedings of the Conference Algoritmy 2024, 76-84. Bratislava: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs Haasdonk B, Kleikamp H, Ohlberger M, Schindler F, Wenzel T (2023) In: SIAM Journal on Scientific Computing, 45(3), A1039-1065. doi:10.1137/22M1493318 Research article (journal) \| Peer reviewed \| Published
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows Gavrilenko Pavel, Haasdonk Bernard, Iliev Oleg, Ohlberger Mario, Schindler Felix, Toktaliev Pavel, Wenzel Tizian, Youssef Maha (2022) In: Lirkov Ivan, Margenov Svetozar (eds.), Large-Scale Scientific Computing, 378-386. Cham: . Research article (book contribution) \| Peer reviewed \| Published
An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization In: , -. \| Peer reviewed
On the Performance of Machine Learning Methods for Breakthrough Curve Prediction In: .
An adaptive model hierarchy for data-augmented training of kernel models for reactive flow In: (eds.), , -. \| Peer reviewed
Localized model reduction for parameterized problems In: (eds.), , -. \| Peer reviewed
A non-conforming dual approach for adaptive Trust-Region Reduced Basis approximation of PDE-constrained optimization In: . \| Peer reviewed

Banholzer S, Keil T, Mechelli L, Ohlberger M, Schindler F, Volkwein S

(2022)

Pure and Applied Functional Analysis

, 7

(5)

1561

1596

Research article (journal)

| Published

Fokina, D, Iliev O, Toktaliev P, Oseledets I, Schindler F

(2022)

((Bitte Journal prüfen))

, 2204.11719

doi:10.48550/arXiv.2204.11719

Research article (journal)

| submitted / under review

Haasdonk B, Ohlberger M, Schindler F

(2022)

Breitenecker, Felix; Kemmetmüller, Wolfgang; Körner, Andreas; Kugi, Andreas; Troch, Inge

MATHMOD 2022 Discussion Contribution Volume

Vienna:

Research article in edited proceedings (conference)

| Published

Buhr Andreas, Iapichino Laura, Ohlberger Mario, Rave Stephan, Schindler Felix, Smetana Kathrin

(2021)

Benner P, Grivet-Talocia S, Quarteroni A, Rozza G, Schilders W, Sileira L

Model Order Reduction: Volume 2 Snapshot-Based Methods and Algorithms

245

306

Research article (book contribution)

| Published

Keil T, Mechelli L, Ohlberger M, Schindler F, Volkwein S

(2021)

ESAIM: Mathematical Modelling and Numerical Analysis

, 55

doi:10.1051/m2an/2021019

Research article (journal)

| Published

Springer Nature

doi:10.1007/978-3-031-56208-2_11

Springer Nature

doi:10.1007/978-3-031-56208-2_10

/ Jednota slovenských matematikov a fyzikov

Springer VDI Verlag

doi:10.1007/978-3-030-97549-4_43

ARGESIM Verlag. doi:10.11128/arep.17.a17155

(kein Verlag angegeben). doi:10.1515/9783110671490-006

Application of Deep Kernel Models for Certified and Adaptive RB-ML-ROM Surrogate Modeling Wenzel, Tizian; Haasdonk, Bernard; Kleikamp, Hendrik; Ohlberger, Mario; Schindler, Felix (2024) In: Lirkov, I.; Margenov, S. (eds.), Large-Scale Scientific Computations, 117-125. Cham: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
Adaptive Localized Reduced Basis Methods for Large Scale PDE-constrained Optimization Keil, Tim; Ohlberger, Mario; Schindler, Felix (2024) In: Lirkov, I; Margenov, S (eds.), Large-Scale Scientific Computations, 108-116. Cham: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
Local training and enrichment based on a residual localization strategy Tim Keil, Mario Ohlberger, Felix Schindler, Julia Schleuß (2024) In: Frolkovič, P; Mikula, K; Ševčovič, D (eds.), Proceedings of the Conference Algoritmy 2024, 76-84. Bratislava: . Research article in edited proceedings (conference) \| Peer reviewed \| Published
A new certified hierarchical and adaptive RB-ML-ROM surrogate model for parametrized PDEs Haasdonk B, Kleikamp H, Ohlberger M, Schindler F, Wenzel T (2023) In: SIAM Journal on Scientific Computing, 45(3), A1039-1065. doi:10.1137/22M1493318 Research article (journal) \| Peer reviewed \| Published
A full order, reduced order and machine learning model pipeline for efficient prediction of reactive flows Gavrilenko Pavel, Haasdonk Bernard, Iliev Oleg, Ohlberger Mario, Schindler Felix, Toktaliev Pavel, Wenzel Tizian, Youssef Maha (2022) In: Lirkov Ivan, Margenov Svetozar (eds.), Large-Scale Scientific Computing, 378-386. Cham: . Research article (book contribution) \| Peer reviewed \| Published
An adaptive projected Newton non-conforming dual approach for trust-region reduced basis approximation of PDE-constrained parameter optimization In: , -. \| Peer reviewed
On the Performance of Machine Learning Methods for Breakthrough Curve Prediction In: .
An adaptive model hierarchy for data-augmented training of kernel models for reactive flow In: (eds.), , -. \| Peer reviewed
Localized model reduction for parameterized problems In: (eds.), , -. \| Peer reviewed
A non-conforming dual approach for adaptive Trust-Region Reduced Basis approximation of PDE-constrained optimization In: . \| Peer reviewed