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| Hauptseite > Publikationsdatenbank > Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization > print |
| Hauptseite > Publikationsdatenbank > Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization > print |
| 001 | 1052737 | ||
| 005 | 20260127203445.0 | ||
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| 100 | 1 | _ | |a Mayfrank, Daniel |0 P:(DE-Juel1)192151 |b 0 |
| 111 | 2 | _ | |a 14th IFAC Symposium on Dynamics and Control of Process Systems, including Biosystems DYCOPS 2025 |c Bratislava |d 2025-06-16 - 2025-06-19 |w Slovakia |
| 245 | _ | _ | |a Task-optimal data-driven surrogate models for eNMPC via differentiable simulation and optimization |
| 260 | _ | _ | |a München [u.a.] |c 2025 |b Elsevier |
| 300 | _ | _ | |a 43-48 |
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| 700 | 1 | _ | |a Dahmen, Manuel |0 P:(DE-Juel1)172097 |b 3 |e Corresponding author |u fzj |
| 773 | _ | _ | |a 10.1016/j.ifacol.2025.07.119 |g Vol. 59, no. 6, p. 43 - 48 |0 PERI:(DE-600)2839185-8 |n 6 |p 43 - 48 |v 59 |y 2025 |x 1474-6670 |
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