Info
Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023 / / ed. by Martin Weiser, Aswin Kannan, Sebastian Pokutta, Kartikey Sharma, Daniel Walter, Andrea Walther, Konstantin Fackeldey
| Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023 / / ed. by Martin Weiser, Aswin Kannan, Sebastian Pokutta, Kartikey Sharma, Daniel Walter, Andrea Walther, Konstantin Fackeldey |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : De Gruyter, , [2025] |
| Descrizione fisica | 1 online resource (X, 202 p.) |
| Collana | De Gruyter Proceedings in Mathematics |
| Soggetto topico | MATHEMATICS / Optimization |
| Soggetto non controllato | Mathematical optimization, Machine learning, Nonlinear optimization, Discrete optimization, Physics informed learning |
| ISBN |
3-11-137774-1
3-11-137677-X |
| Formato | Materiale a stampa  |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface -- Acknowledgment -- Contents -- A framework to solve inverse problems for parametric PDEs using adaptive finite elements and neural networks -- Generation of value function data for bilevel optimal control and application to hybrid electric vehicle -- Graph neural networks to predict strokes from blood flow simulations -- Capturing the macroscopic behavior of molecular dynamics with membership functions -- Adaptive gradient-enhanced Gaussian process surrogates for inverse problems -- Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems -- Constrained piecewise linear optimization by an active signature method -- Parallel trust-region approaches in neural network training -- Trustworthy optimization learning: a brief overview -- Compression-aware training of neural networks using Frank–Wolfe -- Approximation of generalized frequency response functions via vector fitting -- On the nonsmooth regularity condition LIKQ for different abs-normal representations -- Divergence of the ADAM algorithm with fixed-stepsize: a (very) simple example -- Index |
| Record Nr. | UNISA-996657772003316 |
| Berlin ; ; Boston : , : De Gruyter, , [2025] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023 / / ed. by Martin Weiser, Aswin Kannan, Sebastian Pokutta, Kartikey Sharma, Daniel Walter, Andrea Walther, Konstantin Fackeldey
| Mathematical Optimization for Machine Learning : Proceedings of the MATH+ Thematic Einstein Semester 2023 / / ed. by Martin Weiser, Aswin Kannan, Sebastian Pokutta, Kartikey Sharma, Daniel Walter, Andrea Walther, Konstantin Fackeldey |
| Edizione | [1st ed.] |
| Pubbl/distr/stampa | Berlin ; ; Boston : , : De Gruyter, , [2025] |
| Descrizione fisica | 1 online resource (X, 202 p.) |
| Collana | De Gruyter Proceedings in Mathematics |
| Soggetto topico | MATHEMATICS / Optimization |
| Soggetto non controllato | Mathematical optimization, Machine learning, Nonlinear optimization, Discrete optimization, Physics informed learning |
| ISBN |
3-11-137774-1
3-11-137677-X |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Frontmatter -- Preface -- Acknowledgment -- Contents -- A framework to solve inverse problems for parametric PDEs using adaptive finite elements and neural networks -- Generation of value function data for bilevel optimal control and application to hybrid electric vehicle -- Graph neural networks to predict strokes from blood flow simulations -- Capturing the macroscopic behavior of molecular dynamics with membership functions -- Adaptive gradient-enhanced Gaussian process surrogates for inverse problems -- Multifidelity domain decomposition-based physics-informed neural networks and operators for time-dependent problems -- Constrained piecewise linear optimization by an active signature method -- Parallel trust-region approaches in neural network training -- Trustworthy optimization learning: a brief overview -- Compression-aware training of neural networks using Frank–Wolfe -- Approximation of generalized frequency response functions via vector fitting -- On the nonsmooth regularity condition LIKQ for different abs-normal representations -- Divergence of the ADAM algorithm with fixed-stepsize: a (very) simple example -- Index |
| Record Nr. | UNINA-9911024074903321 |
| Berlin ; ; Boston : , : De Gruyter, , [2025] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||
Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors
| Modeling, simulation and optimization in the health- and energy-sector / / René Pinnau, Nicolas R. Gauger, Axel Klar, editors |
| Pubbl/distr/stampa | Cham, Switzerland : , : Springer, , [2022] |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina | 511.8 |
| Collana | SEMA SIMAI Springer series |
| Soggetto topico |
Mathematical models
Mathematical optimization Models matemàtics Optimització matemàtica |
| Soggetto genere / forma |
Congressos
Llibres electrònics |
| ISBN | 3-030-99983-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Record Nr. | UNISA-996479367703316 |
| Cham, Switzerland : , : Springer, , [2022] | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. di Salerno | ||
| ||
Modeling, Simulation and Optimization in the Health- and Energy-Sector / / edited by René Pinnau, Nicolas R. Gauger, Axel Klar
| Modeling, Simulation and Optimization in the Health- and Energy-Sector / / edited by René Pinnau, Nicolas R. Gauger, Axel Klar |
| Edizione | [1st ed. 2022.] |
| Pubbl/distr/stampa | Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 |
| Descrizione fisica | 1 online resource (153 pages) |
| Disciplina |
511.8
362.1068 |
| Collana | ICIAM 2019 SEMA SIMAI Springer Series |
| Soggetto topico |
Mathematics
Mathematics - Data processing Mathematical analysis Mathematical optimization Medical sciences Applications of Mathematics Computational Mathematics and Numerical Analysis Analysis Optimization Health Sciences |
| ISBN | 3-030-99983-1 |
| Formato | Materiale a stampa |
| Livello bibliografico | Monografia |
| Lingua di pubblicazione | eng |
| Nota di contenuto | Part I Prognostic MR Thermometry for Thermal Ablation of Liver Tumours -- 1 Sebastian Blauth et al., Mathematical Modeling and Simulation of Laser-Induced Thermotherapy for the Treatment of Liver Tumors -- 2 Matthias Andres and René Pinnau, The Cattaneo Model for Laser-Induced Thermotherapy: Identification of the Blood-Perfusion Rate -- 3 Kevin Tolle and Nicole Marheineke, On Online Parameter Identification in Laser-Induced Thermotherapy -- Part II Energy-efficient High Temperature Processes via Shape Optimisation -- 4 Robert Feßler at al., Feasibility Study on Simulating a 3D Furnace Including the Effects of Reactions and Vaporization -- 5 Thomas Marx et al., Shape Optimization for the SP1–Model for Convective Radiative Heat Transfer- 6 Nicolas Dietrich et al., Diffusive Radiation Models for Optimal Shape Design in Phosphate Production -- 7 Ruben Sanchez at al., Adjoint-based sensitivity analysis in high-temperature fluid flows with participating media. |
| Record Nr. | UNINA-9910574856003321 |
| Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022 | ||
| Materiale a stampa | ||
| Lo trovi qui: Univ. Federico II | ||
| ||