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*Georgia Institute of Technology – Fridays 2 pm ET*
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**Designers:** Andrew Rosemberg & Michael Klamkin
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**Instructor of Record:** Prof. Pascal Van Hentenryck
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**Instructor:** Prof. Pascal Van Hentenryck
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## Overview
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This student-led course explores modern techniques for controlling — and learning to control — dynamical systems. Topics range from classical optimal control and numerical optimization to reinforcement learning, PDE-constrained optimization (finite-element methods, Neural DiffEq, PINNs, neural operators) and GPU-accelerated workflows.
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This student-led course explores modern techniques for controlling — and learning to control — dynamical systems. Topics range from classical optimal control and numerical optimization to reinforcement learning, PDE-constrained optimization (finite-element methods, Neural DiffEq, PINNs, neural operators), and GPU-accelerated workflows.
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## Prerequisites
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* Solid linear-algebra background
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| 5 | 09/19/2025 | Lecture | **Nonlinear** trajectory **optimization**; collocation; implicit integration | |
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| 6 | 09/26/2025 | **External seminar 1** TBD (speaker to be confirmed) | Trajectory **optimization** on robots in Julia Robotics | |
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| 7 | 10/03/2025 | Lecture | Essentials of PDEs for control engineers; weak forms; FEM/FDM review | |
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| 8 | 10/10/2025 | **External seminar 2** TBD (speaker to be confirmed) | |
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| 9 | 10/17/2025 | **External seminar 3 — François Pacaud** | GPU acceleration of solvers; automatic differentiation on GPUs | |
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| 8 | 10/10/2025 | **External seminar 2** TBD (speaker to be confirmed) | Topology **optimization** | |
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| 9 | 10/17/2025 | **External seminar 3 — François Pacaud** | GPU-accelerated optimal control | |
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|10 | 10/24/2025 | Lecture | Physics-Informed Neural Networks (PINNs): formulation & pitfalls | |
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|11 | 10/31/2025 | **External seminar 4** TBD (speaker to be confirmed) | Neural Differential Equations: PINNs + classical solvers | |
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|12 | 11/07/2025 | Lecture | Neural operators (FNO, Galerkin Transformer); large-scale surrogates | |

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