Abstract Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development.Recently, 2649-20 machine learning applications, including deep reinforcement learning (RL) and genetic algorithms (GA), have emerged in these real-world optimization tasks.We introduce a new machine learning-based optimization scheme that incorporates physics with the operational objectives.This physics-informed neural network (PINN) could find the optimal path in well-defined systems with less exploration and also was capable of obtaining narrow and unstable solutions that have been challenging with bottom-up approaches wilwood .75 master cylinder like RL or GA.Through an objective function that integrates governing laws, constraints, and goals, PINN enables top-down searches for optimal solutions.
In this study, we showcase the PINN applications to various optimization tasks, ranging from inverting a pendulum, determining the shortest-time path, to finding the swingby trajectory.Through this, we discuss how PINN can be applied in the tasks with different characteristics.