Plenary and keynote talks

[P1] A. Kopaničáková. Hybridizing Scientific Machine Learning Approaches with Classical Numerical Methods. Anticipated plenary talk at European Conference on Scientific Machine Learning with Applications in Science and Engineering, 2027
[P2] A. Kopaničáková. Preconditioning approaches for machine-learning and vice versa. Plenary talk at International Conference on Preconditioning Techniques for Scientific and Industrial Applications, 2026
[P3] A. Kopaničáková. PINNs: theoretical and algorithmic advances. Keynote talk at workshop: HPC and AI convergence at the Exascale era, 2026
[P4] A. Kopaničáková. Towards trustworthy use of scientific machine-learning in large scale numerical simulations. Plenary talk at Leibniz Research Network “Mathematical Modeling and Simulation” days, 2026
[P5] A. Kopaničáková. AI-enhanced numerical simulations. Plenary talk at Mathematical and Scientific Machine-learning conference, 2025
[P6] A. Kopaničáková. Domain-decomposition and machine-learning. Plenary talk at 29th International Conference on Domain Decomposition methods, 2025
[P7] A. Kopaničáková. Machine-learning and large-scale numerical simulations. The first Annual Meeting of EMS activity group on Scientific Machine Learning, 2025
[P8] A. Kopaničáková. Towards trustworthy scientific machine-learning. Approximate computing in Numerical Linear Algebra Workshop of the Chinese Academy of Sciences, 2025
[P9] A. Kopaničáková. Towards trustworthy use of scientific machine-learning in large scale numerical simulations. Plenary talk at 10th European Conference on Numerical Methods in Electromagnetism, 2024
[P10] A. Kopaničáková. Enhancing Training of Deep Neural Networks Using Multi-level and Domain-decomposition Methods. Plenary talk at International Multigrid Conference (IMG), 2022

Invited seminars

[I1] A. Kopaničáková. Training of Deep Neural Networks Using Multilevel and Domain-Decomposition Methods. Invited presentation at Saint-Girons Conference: Generating ideas for a numerical world, 2026
[I2] A. Kopaničáková. Training of Deep Neural Networks Using Multilevel and Domain-Decomposition Methods. Invited seminar at by HPC for Learning by CNRS GDR C4, 2026
[I3] A. Kopaničáková. Towards trustworthy use of scientific machine-learning in large-scale numerical simulations. Invited seminar at Sorbonne Univeristy, 2025
[I4] A. Kopaničáková. Hybridizing iterative methods with scientific machine-learning. Invited talk at Mathematical and Scientific Machine learning conference (Naples, Italy), 2025
[I5] A. Kopaničáková. Operator-learning enhanced preconditioning strategies. Plenary seminar at Breakthroughs in Operator Learning for Partial Differential Equations (Pavia, Italy), 2025
[I6] A. Kopaničáková. Hybridizing operator-learning with iterative methods. Invited seminar at IDEFIX: Inversion of Differential Equations For Imaging and physiX (Paris, France), 2025
[I7] A. Kopaničáková. AI-augmented numerical methods. Invited online seminar at THALES research days, 2025
[I8] A. Kopaničáková, G. Karniadakis. DeepONet Based Preconditioning Strategies For Solving Parametric Linear Systems of Equations. Invited online seminar at CRUNCH group, 2024
[I9] A. Kopaničáková. Training of deep neural networks using multilevel and domain-decompositon strategies. Invited talk at the University of Maryland (Baltimore Country), 2023
[I10] A. Kopaničáková. Training deep neural networks using subspace correction methods. Invited talk at TU Delft (Netherlands), 2023
[I11] A. Kopaničáková, H. Kothari, G. Karniadakis, R. Krause. Enhancing Training of Deep Neural Networks Using Multilevel and Domain Decomposition Strategies. Invited seminar at Division of Applied Mathematics (Brown, USA), 2023
[I12] A. Kopaničáková. Multilevel training of deep neural networks. Invited talk in the group of Prof. S. Bordas (University of Luxemburg, Luxemburg), 2022
[I13] A. Kopaničáková, R. Krause. Multilevel minimization and Deep Residual Networks (ResNets). Invited talk in the group of Prof. M. Jaggi (Swiss Federal Institute of Technology Lausanne, Switzerland), 2020
[I14] A. Kopaničáková, R. Krause. Trust-region based minimization techniques for phase-field fracture simulations. Invited talk in the group of Prof. L. de Lorenzis (Technical University of Braunschweig, Germany), 2019

Selected oral presentations

[T1] A. Kopaničáková. Combining operator learning with large-scale solution strategies. Keynote presentation at World Congress on Computational Mechanics and European Congress on Computational Methods in Applied Sciences and Engineering, 2026
[T2] A. Kopaničáková. DeepONet-based Multiscale methods. International Research Conference on Multi-Grid and Multi-Scale Methods in Computational Science, 2025
[T3] A. Kopaničáková. Nonlinear Preconditioning Techniques for Efficient Phase-Field Fracture Modeling. XI International Conference on Coupled Problems in Science and Engineering, 2025
[T4] A. Kopaničáková. DeepONet-based Preconditioning for Krylov Methods. 3rd IACM Digital Twins in Engineering Conference (DTE 2025) and 1st ECCOMAS Artificial Intelligence and Computational Methods in Applied Science (AICOMAS 2025), 2025
[T5] A. Kopaničáková. Multilevel minimization and deep learning. Algoritmy: Central European Conference on Scientific Computing, 2024
[T6] A. Kopaničáková. Training of deep neural networks using nonlinear multilevel methods. 28th International Domain Decomposition Conference, (DDXXVIII), 2024
[T7] A. Kopaničáková. DeepONet-based Preconditioning for Krylov Methods. International Conference On Preconditioning Techniques For Scientific and Industrial Applications, 2024
[T8] A. Kopaničáková. Accelerating training of physics-informed neural networks using decomposition strategies. International Conference On Preconditioning Techniques For Scientific and Industrial Applications, 2024
[T9] A. Kopaničáková, H. Kothari, R. Krause. Towards Large-Scale Training of Deep Neural Networks Using Domain-Decomposition Methods. SIAM Conference on Computational Science and Engineering (CSE23), 2023
[T10] A. Kopaničáková. Nonlinear multilevel minimization methods with applicaitons in computational science and machine-learning. Rising Stars in Computational & Data Science event, 2023
[T11] A. Kopaničáková, S. Gratton, P. Toint. Multilevel Objective-Function-Free Trust-Region with an Application to Neural Networks Training. Copper Mountain Conference On Multigrid Methods, 2023
[T12] A. Kopaničáková, H. Kothari, G. Karniadakis, R. Krause. 10th International Congress on Industrial and Applied Mathematics. SIAM, 2023
[T13] A. Kopaničáková. Training of Deep Neural Networks Using Multilevel and Domain Decomposition Strategies. Deep learning, image analysis, inverse problems, and optimization workshop, 2023
[T14] A. Kopaničáková, H. Kothari, R. Krause. Nonlinear additive and multiplicative preconditioning strategies for monolithic phase-field fracture models. 8th European Congress on Computational Methods in Applied Sciences and Engineering (ECCOMAS CONGRESS), 2022
[T15] A. Kopaničáková, H. Kothari, R. Krause. Nonlinear preconditioning strategies for monolithic phase-field fracture models. 11th European Solid Mechanics Conference (ESMC), 2022
[T16] A. Kopaničáková, H. Kothari, P. Zulian, R. Krause. Nonlinear multilevel and domain decomposition methods for phase-field fracture simulations in monolithic framework. 27th International Conference on Domain Decomposition Methods (DD27), 2022
[T17] A. Kopaničáková, H. Kothari, P. Zulian, R. Krause. Multilevel and domain decomposition methods for phase-field fracture simulations. 15th World Congress on Computational Mechanics & 8th Asian Pacific Congress on Computational Mechanics (WCCM-APCOM), 2022
[T18] A. Kopaničáková, R. Krause. Multiscale Training Algorithms for Deep Neural Networks. SIAM Mathematics of Data Science Conference (SIAM-MDS), 2022
[T19] A. Kopaničáková, R. Krause. Globally Convergent Multilevel Training of Deep Residual Networks. 20th Copper Mountain Conference On Multigrid Methods, 2021
[T20] A. Kopaničáková, R. Krause. A large scale phase-field fracture simulations. The Platform for Advanced Scientific Computing (PASC) Conference, 2021
[T21] A. Kopaničáková, R. Krause. Affine Similar Trust-Region Method with Application to Phase-Field Models of Brittle Fracture. The US National Congress on Computational Mechanics (USNCCM 16), 2021
[T22] A. Kopaničáková, R. Krause. Multilevel training of deep residual networks. 26th International Domain Decomposition Conference, (DDXXVI), 2020
[T23] A. Kopaničáková, R. Krause, R. Tamstorf. Subdivision-Based nonlinear multiscale cloth simulation. Eccomas Thematic Conference on eXtended Discretization MethodS (X-DMS), 2019
[T24] A. Kopaničáková, R. Krause. A recursive multilevel trust region method with application to fully monolithic phase-field models of brittle fracture. The US National Congress on Computational Mechanics (USNCCM 15), 2019
[T25] A. Kopaničáková, C. Bilgen, K. Weinberg, R. Krause. Recursive multilevel trust region method, application to phase-field fracture. SIAM Conference on Parallel Processing for Scientific Computing (SIAM-PP), 2018
[T26] A. Kopaničáková, R. Krause, R. Tamstorf. Subdivision-based nonlinear multiscale cloth simulations. Copper Mountain Conference On Iterative Methods, 2018
[T27] A. Kopaničáková, R. Krause. Recursive multilevel trust region strategy with application to phase-field fracture. The 13th World Congress in Computational Mechanics (WCCMXIII), 2018
[T28] A. Kopaničáková, R. Krause. A non-linear multilevel method for phase-field fracture models. The Platform for Advanced Scientific Computing (PASC) Conference, 2017