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Real-Time PDE-Constrained Optimization
Lorenz T. Biegler,Omar Ghattas,Matthias Heinkenschloss,David E. Keyes,Bart G. van Bloemen Waanders +4 more
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TLDR
A real-time, large scale optimization of water network systems using a subdomain approach Index and the effect of the digital filter stepsize control on control optimization performance is described.Abstract:
Preface Part I. Concepts and Properties of Real-Time, Online Strategies: 1. Constrained optimal feedback control for DAE 2. A stabilizing real-time implementation of NMPC 3. Numerical feedback controller design for PDE systems using model reduction: techniques and case studies 4. Least-squares methods for optimization Part II. Fast PDE-Constrained Optimization Solvers: 5. Space-time multigrid methods for solving unsteady optimal control problem 6. A time-parallel implicit methodology for the near-real-time solution of systems of linear oscillators 7. Generalized SQP-methods with 'parareal' time-domain decomposition for time-dependent PDE-constrained optimization 8. Simultaneous pseudo-timestepping for state constrained optimization problems in aerodynamics 9. The effect of the digital filter stepsize control on control optimization performance Part III. Reduced Order Modeling: 10. Certified rapid solution of partial differential equations for real-time parameter estimation and optimization 11. WillcoxMOR 12. Feedback control of flow separation Part IV. Applications: 13. Shape and topological sensitivity 14. COFIR: Coarse and fine image registration 15. Real-time, large scale optimization of water network systems using a subdomain approach Index.read more
Citations
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Deep Neural Networks Motivated by Partial Differential Equations
Lars Ruthotto,Eldad Haber +1 more
TL;DR: In this article, a new PDE interpretation of a class of deep convolutional neural networks (CNN) was established, which are commonly used to learn from speech, image, and video data.
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Deep Neural Networks Motivated by Partial Differential Equations
Lars Ruthotto,Eldad Haber +1 more
TL;DR: A new PDE interpretation of a class of deep convolutional neural networks (CNN) that are commonly used to learn from speech, image, and video data is established and three new ResNet architectures are derived that fall into two new classes: parabolic and hyperbolic CNNs.
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Optimal sensitivity based on IPOPT
TL;DR: A flexible, open source implementation that provides the optimal sensitivity of solutions of nonlinear programming (NLP) problems, and is adapted to a fast solver based on a barrier NLP method.
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Enhancement in treatment planning for magnetic nanoparticle hyperthermia: optimization of the heat absorption pattern.
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Efficient direct multiple shooting for nonlinear model predictive control on long horizons
TL;DR: This work addresses direct multiple shooting based algorithms for nonlinear model predictive control, with a focus on problems with long prediction horizons, and describes different efficient multiple shooting variants with a computational effort that is only linear in the horizon length.