Abstract Optimization tasks are essential in modern engineering fields such as chip design, spacecraft trajectory determination, and reactor scenario development. Recently, 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 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. 

Solving real-world optimization tasks using physics-informed neural computing

Abstract For stable and efficient fusion energy production using a tokamak reactor, it is essential to maintain a high-pressure hydrogenic plasma without plasma disruption. Therefore, it is necessary to actively control the tokamak based on the observed plasma state, to manoeuvre high-pressure plasma while avoiding tearing instability, the leading cause of disruptions. This presents an obstacle-avoidance problem for which artificial intelligence based on reinforcement learning has recently shown remarkable performance 1–4 . However, the obstacle here, the tearing instability, is difficult to forecast and is highly prone to terminating plasma operations, especially in the ITER baseline scenario. Previously, we developed a multimodal dynamic model that estimates the likelihood of future tearing instability based on signals from multiple diagnostics and actuators 5 . Here we harness this dynamic model as a training environment for reinforcement-learning artificial intelligence, facilitating automated instability prevention. We demonstrate artificial intelligence control to lower the possibility of disruptive tearing instabilities in DIII-D 6 , the largest magnetic fusion facility in the United States. The controller maintained the tearing likelihood under a given threshold, even under relatively unfavourable conditions of low safety factor and low torque. In particular, it allowed the plasma to actively track the stable path within the time-varying operational space while maintaining H-mode performance, which was challenging with traditional preprogrammed control. This controller paves the path to developing stable high-performance operational scenarios for future use in ITER. 

Avoiding fusion plasma tearing instability with deep reinforcement learning

Plasma with an internal transport barrier (ITB) will be developed in JT-60SA as an attractive operation appropriate for a steady-state fusion reactor. To achieve the ITB plasma while avoiding magnetohydrodynamic instabilities, it is advantageous to simultaneously control the safety factor (q) profile and the normalized beta ( βN ). In this study, a control system for simultaneous control of the q profile and βN is studied in simulations prior to the real experiment in JT-60SA. The bootstrap current dominates the total current in the ITB region, which results in a coupling between the pressure profile and the q profile. Thus, it is crucial to control the q profile and βN according to the strength of the ITB. A two-stage neural network (NN)-based control system was developed to address this problem. The first stage estimates the transport properties (i.e. ITB strength) of the plasma from measurements. The second stage consists of several NNs for control of the q profile and βN . According to the ITB strength estimated by the NN in the first stage, the appropriate NN for control is selected from those in the second stage. Each NN in the second stage is trained to control plasmas with different ITB strengths through reinforcement learning employing RAPTOR, an integrated transport code. To validate this system, it is tested in a simulation employing another integrated transport code, TOPICS, to mimic the plasma control in JT-60SA plasmas with various ITB strengths. Stable control of the q profile and βN is achieved in ITB plasmas simulated by both the RAPTOR and TOPICS codes.

https://iopscience.iop.org/article/10.1088/1741-4326/acd393/pdf

Simultaneous control of safety factor profile and normalized beta for JT-60SA using reinforcement learning

Kinetic equilibrium reconstruction plays a vital role in the physical analysis of plasma stability and control in fusion tokamaks. However, the traditional approach is subjective and prone to human biases. To address this, the consistent automatic kinetic equilibrium reconstruction (CAKE) method was introduced, providing objective results. Nonetheless, its offline nature limits its application in real-time plasma control systems (PCSs). To address this limitation, we present RTCAKENN, a machine learning model that approximates 7 CAKE-level output profiles, namely pressure, inverse q, toroidal current density, electron temperature and density, carbon ion impurity temperature and rotation profiles, using real-time available inputs. The deep neural network consists of an encoder layer, where the scalars and interdependent inputs such as plasma boundary coordinates and motional Stark effect data are encoded using multi-layer perceptrons (MLPs), while profile inputs are encoded by 1D convolutional layers. The encoded data is passed through a MLP for latent feature extraction, before being decoded in the decoding layers, which consist of upsampling and convolutional layers. RTCAKENN has been implemented in the DIII-D PCS and our model achieves accuracy comparable to CAKE and surpasses existing real-time alternatives. Through clever dropout training, RTCAKENN exhibits robustness and can operate even in the absence of Thomson scattering data or charge exchange recombination data. It executes in under 8 ms in the real-time environment, enabling future application in real-time control and analysis.

Machine learning-based real-time kinetic profile reconstruction in DIII-D

This letter provides a new physical insight into the fast ion effects on turbulence in plasmas having a high fast ion fraction and peaked fast ion density profile. We elucidate turbulence stabilization mechanisms by fast ions that result in internal transport barrier formation in the fast ion regulated enhancement mode plasma. Both linear and nonlinear gyrokinetic simulations show that the dominant turbulence suppression mechanisms are the dilution effects. In particular, we find that turbulence can be sufficiently suppressed solely by an inverted main ion density gradient due to fast ions, for the first time. New physical findings reported here improve our understanding of fast ion effects on turbulence, essential for fusion energy production where . Moreover, they will open up a new methodology to control plasma turbulence applicable to a wide range of plasma confinement regimes.

Turbulence stabilization in tokamak plasmas with high population of fast ions

Nuclear fusion is one of the most attractive alternatives to carbon-dependent energy sources1. Harnessing energy from nuclear fusion in a large reactor scale, however, still presents many scientific challenges despite the many years of research and steady advances in magnetic confinement approaches. State-of-the-art magnetic fusion devices cannot yet achieve a sustainable fusion performance, which requires a high temperature above 100 million kelvin and sufficient control of instabilities to ensure steady-state operation on the order of tens of seconds2,3. Here we report experiments at the Korea Superconducting Tokamak Advanced Research4 device producing a plasma fusion regime that satisfies most of the above requirements: thanks to abundant fast ions stabilizing the core plasma turbulence, we generate plasmas at a temperature of 100 million kelvin lasting up to 20 seconds without plasma edge instabilities or impurity accumulation. A low plasma density combined with a moderate input power for operation is key to establishing this regime by preserving a high fraction of fast ions. This regime is rarely subject to disruption and can be sustained reliably even without a sophisticated control, and thus represents a promising path towards commercial fusion reactors. A magnetic confinement regime established at the Korea Superconducting Tokamak Advanced Research device enables the generation of plasmas over 108 kelvin for 20 seconds with the aid of fast ions without plasma edge instabilities or impurity accumulation. 

/pdf/a-sustained-high-temperature-fusion-plasma-regime-1h0hw989.pdf

A sustained high-temperature fusion plasma regime facilitated by fast ions

This work develops an artificially intelligent (AI) tokamak operation design algorithm that provides an adequate operation trajectory to control multiple plasma parameters simultaneously into different targets. An AI is trained with the reinforcement learning technique in the data-driven tokamak simulator, searching for the best action policy to get a higher reward. By setting the reward function to increase as the achieved β p, q 95, and l i are close to the given target values, the AI tries to properly determine the plasma current and boundary shape to reach the given targets. After training the AI with various targets and conditions in the simulation environment, we demonstrated that we could successfully achieve the target plasma states with the AI-designed operation trajectory in a real KSTAR experiment. The developed algorithm would replace the human task of searching for an operation setting for given objectives, provide clues for developing advanced operation scenarios, and serve as a basis for the autonomous operation of a fusion reactor.

Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR

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A sustained high-temperature fusion plasma regime facilitated by fast ions

Development of an operation trajectory design algorithm for control of multiple 0D parameters using deep reinforcement learning in KSTAR