https://logic.kastel.kit.edu/teaching/Recent content in Teaching on Logic of Autonomous Dynamical SystemsHugoen-ushttps://logic.kastel.kit.edu/teaching/2023_1-constlog/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2023_1-constlog/https://logic.kastel.kit.edu/teaching/2024_1-constlog/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2024_1-constlog/https://logic.kastel.kit.edu/teaching/2022-ws-cps/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2022-ws-cps/https://logic.kastel.kit.edu/teaching/2023-ws-cps/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2023-ws-cps/https://logic.kastel.kit.edu/teaching/2024-ws-cps/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2024-ws-cps/https://logic.kastel.kit.edu/teaching/2024-ws-propa/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2024-ws-propa/https://logic.kastel.kit.edu/teaching/2023_1-proseminar-diffprogramming/Mon, 01 Jan 0001 00:00:00 +0000https://logic.kastel.kit.edu/teaching/2023_1-proseminar-diffprogramming/Differentiable Programming is the idea that many programs describe differentiable functions and that their derivatives can be computed algorithmically through automatic differentiation. Combined with gradient-based optimization methods this is a powerful method to learn parameters, that generalizes approaches in machine learning from neural networks to arbitrary differentiable programs. In this seminar we take differentiable programming as a starting point to explore some of the related ideas in optimization and automatic differentiation, as well as various applications in, among others, control, physics, simulation and computer graphics.